diff --git a/examples/benchmarks/1/ph1-bm1-sc1.py b/examples/benchmarks/1/ph1-bm1-sc1.py
new file mode 100644
index 000000000..b8cc5e3b6
--- /dev/null
+++ b/examples/benchmarks/1/ph1-bm1-sc1.py
@@ -0,0 +1,258 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [centre, focus]
+
+bowl_coords = [centre, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0
+density = 1000.0 
+alpha_coeff = 0.0
+
+# non-dispersive
+alpha_power = 2.0
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# bowl openning [m]
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0 / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm1_sc1_input.h5'
+output_filename = 'ph1_bm1_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM1-SC1')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx - 1) // 2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM1-SC1')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/1/ph1-bm1-sc2.py b/examples/benchmarks/1/ph1-bm1-sc2.py
new file mode 100644
index 000000000..c118e74d8
--- /dev/null
+++ b/examples/benchmarks/1/ph1-bm1-sc2.py
@@ -0,0 +1,245 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+radius: float = 10.0e-3
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0
+density = 1000.0 
+alpha_coeff = 0.0
+
+# non-dispersive
+alpha_power = 2.0
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0 / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# position of planar element
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+# add element
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm1_sc2_input.h5'
+output_filename = 'ph1_bm1_sc2_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM1-SC2')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx - 1) // 2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM1-SC2')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/2/ph1-bm2-sc1.py b/examples/benchmarks/2/ph1-bm2-sc1.py
new file mode 100644
index 000000000..70079bf72
--- /dev/null
+++ b/examples/benchmarks/2/ph1-bm2-sc1.py
@@ -0,0 +1,258 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [centre, focus]
+
+bowl_coords = [centre, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 
+density = 1000.0 
+alpha_coeff = 100.0 
+
+# non-dispersive
+alpha_power = 2.0
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# bowl openning [m]
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0 / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm2_sc1_input.h5'
+output_filename = 'ph1_bm2_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM2-SC1')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM2-SC1')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/2/ph1-bm2-sc2.py b/examples/benchmarks/2/ph1-bm2-sc2.py
new file mode 100644
index 000000000..3aa07ab09
--- /dev/null
+++ b/examples/benchmarks/2/ph1-bm2-sc2.py
@@ -0,0 +1,247 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 
+density = 1000.0 
+alpha_coeff = 100.0 
+
+# non-dispersive
+alpha_power = 2.0
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# radius of planar element
+radius: float = 10e-3 
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0 / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# positions of planar element
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+# add element
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm2_sc2_input.h5'
+output_filename = 'ph1_bm2_sc2_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM2-SC2')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM2-SC2')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/3/ph1-bm3-sc1.ipynb b/examples/benchmarks/3/ph1-bm3-sc1.ipynb
new file mode 100644
index 000000000..e6e7222e8
--- /dev/null
+++ b/examples/benchmarks/3/ph1-bm3-sc1.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "20662708-c2b3-46e9-85fb-15270dff5f8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "approximate size of source matrix: 2.11 MB (float32 precision)\n",
+      "total computation time: 0.20s\n",
+      "Running k-Wave simulation...\n",
+      "  start time: 13-Mar-2024-09-16-31\n",
+      "set_flags_from_list\n",
+      "cool\n",
+      "  dt: 17.857143ns, t_end: 93.053571us, time steps: 5212\n",
+      "  input grid size: 141 by 241 grid points (70.5mm by 120.5mm)\n",
+      "  maximum supported frequency: 1.489362MHz by 1.493776MHz\n",
+      "  expanding computational grid...\n",
+      "  computational grid size: 181 by 281 grid points\n",
+      "0\n",
+      "1\n",
+      "  precomputation completed in 0.0444539s\n",
+      "  saving input files to disk...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dsinden\\Documents\\GitLab\\k-wave-python\\kwave\\kWaveSimulation.py:1323: UserWarning: WARNING: Highest prime factors in each dimension are [181 281]\n",
+      "  warn(f'WARNING: Highest prime factors in each dimension are {prime_facs}')\n",
+      "C:\\Users\\dsinden\\Documents\\GitLab\\k-wave-python\\kwave\\kWaveSimulation.py:1324: UserWarning: Use dimension sizes with lower prime factors to improve speed\n",
+      "  warn('Use dimension sizes with lower prime factors to improve speed')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  completed in 0.2312281s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "!pip install k-wave-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4282a367-8c38-4a4c-90fc-7a245bf438b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from kwave.data import Vector\n",
+    "from kwave.utils.kwave_array import kWaveArray\n",
+    "from kwave.utils.checks import check_stability\n",
+    "from kwave.kgrid import kWaveGrid\n",
+    "from kwave.kmedium import kWaveMedium\n",
+    "from kwave.ksource import kSource\n",
+    "from kwave.ksensor import kSensor\n",
+    "from kwave.utils.signals import create_cw_signals\n",
+    "from kwave.utils.filters import extract_amp_phase\n",
+    "from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu\n",
+    "\n",
+    "from kwave.options.simulation_options import SimulationOptions\n",
+    "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8164b2ca-efe1-4f33-a83d-9cfcc5ad6aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nx: int = 141\n",
+    "Nz: int = 241\n",
+    "\n",
+    "dx: float = 0.5e-3\n",
+    "dz: float = dx\n",
+    "\n",
+    "focus:int = 128\n",
+    "\n",
+    "focus_coords = [(Nx - 1) // 2, focus]\n",
+    "\n",
+    "bowl_coords = [(Nx - 1) // 2, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdecae6-abf5-4b06-ad02-2a77af799de3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE MATERIAL PROPERTIES\n",
+    "# =========================================================================\n",
+    "\n",
+    "# water\n",
+    "sound_speed = 1500.0 * np.ones((Nx, Nz))\n",
+    "density = 1000.0 * np.ones((Nx, Nz))\n",
+    "alpha_coeff = np.zeros((Nx, Nz))\n",
+    "\n",
+    "# non-dispersive\n",
+    "alpha_power = 2.0\n",
+    "\n",
+    "# cortical bone\n",
+    "sound_speed[:, 60:74] = 2800.0\n",
+    "density[:, 60:74] = 1850.0\n",
+    "alpha_coeff[:, 60:74] = 4.0\n",
+    "\n",
+    "c0_min = np.min(np.ravel(sound_speed))\n",
+    "c0_max = np.max(np.ravel(sound_speed))\n",
+    "\n",
+    "medium = kWaveMedium(sound_speed=sound_speed,\n",
+    "                     density=density,\n",
+    "                     alpha_coeff=alpha_coeff,\n",
+    "                     alpha_power=alpha_power)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51b2b4e3-30dc-4a1b-aca5-0c9fe8dbe039",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE TRANSDUCER SETUP\n",
+    "# =========================================================================\n",
+    "\n",
+    "# bowl radius of curvature [m]\n",
+    "source_roc: float = 64.0e-3\n",
+    "\n",
+    "# as we will use the bowl element this has to be a int or float\n",
+    "diameters: float = 64.0e-3\n",
+    "\n",
+    "# frequency [Hz]\n",
+    "freq = 500e3\n",
+    "\n",
+    "# source pressure [Pa]\n",
+    "source_amp = np.array([60e3])\n",
+    "\n",
+    "# phase [rad]\n",
+    "source_phase = np.array([0.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c64874d-5718-4f2d-ad47-954417ca9706",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE COMPUTATIONAL PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "useMaxTimeStep: bool = True\n",
+    "\n",
+    "# wavelength\n",
+    "k_min: float = c0_min / freq\n",
+    "\n",
+    "# points per wavelength\n",
+    "ppw: float = k_min / dx\n",
+    "\n",
+    "# number of periods to record\n",
+    "record_periods: int = 3\n",
+    "\n",
+    "# compute points per period\n",
+    "ppp: int = 60\n",
+    "\n",
+    "# CFL number determines time step\n",
+    "cfl: float = (ppw / ppp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaacacf8-cce0-4e7d-b76c-e87ad1236377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE KGRID\n",
+    "# =========================================================================\n",
+    "\n",
+    "grid_size_points = Vector([Nx, Nz])\n",
+    "\n",
+    "grid_spacing_meters = Vector([dx, dz])\n",
+    "\n",
+    "# create the k-space grid\n",
+    "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b181b1d1-b13f-40b2-9271-84e2d0658c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE TIME VECTOR\n",
+    "# =========================================================================\n",
+    "\n",
+    "# compute corresponding time stepping\n",
+    "dt = 1.0 / (ppp * freq)\n",
+    "\n",
+    "# compute corresponding time stepping\n",
+    "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+    "\n",
+    "dt_stability_limit = check_stability(kgrid, medium)\n",
+    "\n",
+    "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):\n",
+    "    dt_old = dt\n",
+    "    ppp = np.ceil(1.0 / (dt_stability_limit * freq))\n",
+    "    dt = 1.0 / (ppp * freq)\n",
+    "\n",
+    "# calculate the number of time steps to reach steady state\n",
+    "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+    "\n",
+    "# create the time array using an integer number of points per period\n",
+    "Nt = round(t_end / dt)\n",
+    "\n",
+    "# make time array\n",
+    "kgrid.setTime(Nt, dt)\n",
+    "\n",
+    "# calculate the actual CFL after adjusting for dt\n",
+    "cfl_actual = 1.0 / (dt * freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871a4da9-e2c6-42e2-806c-ef5b7d0d83f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SOURCE PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "# create empty kWaveArray this specfies the transducer properties\n",
+    "karray = kWaveArray(bli_tolerance=0.01,\n",
+    "                    upsampling_rate=16,\n",
+    "                    single_precision=True)\n",
+    "\n",
+    "# set bowl position and orientation\n",
+    "bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),\n",
+    "            kgrid.y_vec[bowl_coords[1]].item()]\n",
+    "\n",
+    "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+    "             kgrid.y_vec[focus_coords[1]].item()]\n",
+    "\n",
+    "# add bowl shaped element\n",
+    "karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)\n",
+    "\n",
+    "# create time varying source\n",
+    "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+    "                               freq,\n",
+    "                               source_amp,\n",
+    "                               source_phase)\n",
+    "\n",
+    "# make a source object.\n",
+    "source = kSource()\n",
+    "\n",
+    "# assign binary mask using the karray\n",
+    "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+    "\n",
+    "# assign source pressure output in time\n",
+    "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a163bb4-585b-4dbc-a3fe-eac35d355d34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SENSOR PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "sensor = kSensor()\n",
+    "\n",
+    "# set sensor mask: the mask says at which points data should be recorded\n",
+    "sensor.mask = np.ones((Nx, Nz), dtype=bool)\n",
+    "\n",
+    "# set the record type: record the pressure waveform\n",
+    "sensor.record = ['p']\n",
+    "\n",
+    "# record the final few periods when the field is in steady state\n",
+    "sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70482df0-7ccc-4e42-b00d-88e8952e28f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SIMULATION PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "DATA_CAST = 'single'\n",
+    "DATA_PATH = './'\n",
+    "\n",
+    "input_filename = 'ph1_bm3_sc1_input.h5'\n",
+    "output_filename = 'ph1_bm3_sc1_output.h5'\n",
+    "\n",
+    "# options for writing to file, but not doing simulations\n",
+    "simulation_options = SimulationOptions(\n",
+    "    data_cast=DATA_CAST,\n",
+    "    data_recast=True,\n",
+    "    save_to_disk=True,\n",
+    "    input_filename=input_filename,\n",
+    "    output_filename=output_filename,\n",
+    "    save_to_disk_exit=False,\n",
+    "    data_path=DATA_PATH,\n",
+    "    pml_inside=False)\n",
+    "\n",
+    "execution_options = SimulationExecutionOptions(\n",
+    "    is_gpu_simulation=True,\n",
+    "    delete_data=False,\n",
+    "    verbose_level=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d698217-80ac-42b2-bf58-d0fb8632cb40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# RUN THE SIMULATION\n",
+    "# =========================================================================\n",
+    "\n",
+    "sensor_data = kspace_first_order_2d_gpu(\n",
+    "    medium=medium,\n",
+    "    kgrid=kgrid,\n",
+    "    source=source,\n",
+    "    sensor=sensor,\n",
+    "    simulation_options=simulation_options,\n",
+    "    execution_options=execution_options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "026657f3-96d8-44b2-aeed-b8ca241285d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# VISUALIZATION\n",
+    "# =========================================================================\n",
+    "\n",
+    "# sampling frequency\n",
+    "fs = 1.0 / kgrid.dt\n",
+    "\n",
+    "# get Fourier coefficients\n",
+    "amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')\n",
+    "\n",
+    "# reshape to array\n",
+    "p = np.reshape(amp, (Nx, Nz), order='F')\n",
+    "\n",
+    "# axes for plotting\n",
+    "x_vec = kgrid.x_vec\n",
+    "y_vec = kgrid.y_vec[0] - kgrid.y_vec\n",
+    "\n",
+    "fig1, ax1 = plt.subplots(1, 1)\n",
+    "p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),\n",
+    "                    1e3 * np.squeeze(y_vec),\n",
+    "                    np.flip(p.T, axis=1) / 1e3,\n",
+    "                    shading='gouraud', cmap='viridis')\n",
+    "ax1.set(xlabel='Lateral Position [mm]',\n",
+    "        ylabel='Axial Position [mm]',\n",
+    "        title='PH1-BM3-SC1')\n",
+    "ax1.set_aspect('equal')\n",
+    "cbar1 = fig1.colorbar(p1, ax=ax1)\n",
+    "_ = cbar1.ax.set_title('[kPa]', fontsize='small')\n",
+    "\n",
+    "fig2, ax2 = plt.subplots(1, 1)\n",
+    "ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )\n",
+    "ax2.set(xlabel='Axial Position [mm]',\n",
+    "        ylabel='Pressure [kPa]',\n",
+    "        title='PH1-BM3-SC1')\n",
+    "ax2.grid(True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd4be3b3-5c8b-4b0c-aa08-5a3005af9572",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82d8b1d8-b793-4202-87f9-bb7f9c1f3da8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/benchmarks/3/ph1-bm3-sc1.py b/examples/benchmarks/3/ph1-bm3-sc1.py
new file mode 100644
index 000000000..106385c2d
--- /dev/null
+++ b/examples/benchmarks/3/ph1-bm3-sc1.py
@@ -0,0 +1,268 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [(Nx - 1) // 2, focus]
+
+bowl_coords = [(Nx - 1) // 2, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+# cortical bone
+sound_speed[:, 60:74] = 2800.0
+density[:, 60:74] = 1850.0
+alpha_coeff[:, 60:74] = 4.0
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# bowl openning [m]
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm3_sc1_input.h5'
+output_filename = 'ph1_bm3_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM3-SC1')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM3-SC1')
+ax2.grid(True)
+
+plt.show()
\ No newline at end of file
diff --git a/examples/benchmarks/3/ph1-bm3-sc2.ipynb b/examples/benchmarks/3/ph1-bm3-sc2.ipynb
new file mode 100644
index 000000000..abffd2150
--- /dev/null
+++ b/examples/benchmarks/3/ph1-bm3-sc2.ipynb
@@ -0,0 +1,657 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "20662708-c2b3-46e9-85fb-15270dff5f8a",
+      "metadata": {
+        "id": "20662708-c2b3-46e9-85fb-15270dff5f8a",
+        "outputId": "cf4ca1b8-7d98-4699-833d-1a403b73544b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting k-wave-python\n",
+            "  Downloading k_wave_python-0.3.2-py3-none-any.whl (212 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.3/212.3 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting beartype==0.16.4 (from k-wave-python)\n",
+            "  Downloading beartype-0.16.4-py3-none-any.whl (819 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m819.1/819.1 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting deepdiff==6.7.1 (from k-wave-python)\n",
+            "  Downloading deepdiff-6.7.1-py3-none-any.whl (76 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting h5py==3.10.0 (from k-wave-python)\n",
+            "  Downloading h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)\n",
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+            "\u001b[?25hCollecting matplotlib==3.8.3 (from k-wave-python)\n",
+            "  Downloading matplotlib-3.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
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+            "\u001b[?25hCollecting nptyping==2.5.0 (from k-wave-python)\n",
+            "  Downloading nptyping-2.5.0-py3-none-any.whl (37 kB)\n",
+            "Requirement already satisfied: numpy<1.27.0,>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from k-wave-python) (1.25.2)\n",
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+            "\u001b[?25hCollecting scipy==1.12.0 (from k-wave-python)\n",
+            "  Downloading scipy-1.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.4 MB)\n",
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+            "\u001b[?25hCollecting ordered-set<4.2.0,>=4.0.2 (from deepdiff==6.7.1->k-wave-python)\n",
+            "  Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-wave-python) (1.2.0)\n",
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+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-wave-python) (3.1.2)\n",
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+            "Installing collected packages: scipy, ordered-set, opencv-python, nptyping, h5py, beartype, matplotlib, deepdiff, k-wave-python\n",
+            "  Attempting uninstall: scipy\n",
+            "    Found existing installation: scipy 1.11.4\n",
+            "    Uninstalling scipy-1.11.4:\n",
+            "      Successfully uninstalled scipy-1.11.4\n",
+            "  Attempting uninstall: opencv-python\n",
+            "    Found existing installation: opencv-python 4.8.0.76\n",
+            "    Uninstalling opencv-python-4.8.0.76:\n",
+            "      Successfully uninstalled opencv-python-4.8.0.76\n",
+            "  Attempting uninstall: h5py\n",
+            "    Found existing installation: h5py 3.9.0\n",
+            "    Uninstalling h5py-3.9.0:\n",
+            "      Successfully uninstalled h5py-3.9.0\n",
+            "  Attempting uninstall: matplotlib\n",
+            "    Found existing installation: matplotlib 3.7.1\n",
+            "    Uninstalling matplotlib-3.7.1:\n",
+            "      Successfully uninstalled matplotlib-3.7.1\n",
+            "Successfully installed beartype-0.16.4 deepdiff-6.7.1 h5py-3.10.0 k-wave-python-0.3.2 matplotlib-3.8.3 nptyping-2.5.0 opencv-python-4.9.0.80 ordered-set-4.1.0 scipy-1.12.0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "matplotlib",
+                  "mpl_toolkits"
+                ]
+              },
+              "id": "ef1a1871599c4a9e8925d98ceb4d7c02"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "!pip install k-wave-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "4282a367-8c38-4a4c-90fc-7a245bf438b4",
+      "metadata": {
+        "id": "4282a367-8c38-4a4c-90fc-7a245bf438b4"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from kwave.data import Vector\n",
+        "from kwave.utils.kwave_array import kWaveArray\n",
+        "from kwave.utils.checks import check_stability\n",
+        "from kwave.kgrid import kWaveGrid\n",
+        "from kwave.kmedium import kWaveMedium\n",
+        "from kwave.ksource import kSource\n",
+        "from kwave.ksensor import kSensor\n",
+        "from kwave.utils.signals import create_cw_signals\n",
+        "from kwave.utils.filters import extract_amp_phase\n",
+        "from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu\n",
+        "\n",
+        "from kwave.options.simulation_options import SimulationOptions\n",
+        "from kwave.options.simulation_execution_options import SimulationExecutionOptions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "8164b2ca-efe1-4f33-a83d-9cfcc5ad6aff",
+      "metadata": {
+        "id": "8164b2ca-efe1-4f33-a83d-9cfcc5ad6aff"
+      },
+      "outputs": [],
+      "source": [
+        "Nx: int = 141\n",
+        "Nz: int = 241\n",
+        "\n",
+        "dx: float = 0.5e-3\n",
+        "dz: float = dx"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "abdecae6-abf5-4b06-ad02-2a77af799de3",
+      "metadata": {
+        "id": "abdecae6-abf5-4b06-ad02-2a77af799de3"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE MATERIAL PROPERTIES\n",
+        "# =========================================================================\n",
+        "\n",
+        "# water\n",
+        "sound_speed = 1500.0 * np.ones((Nx, Nz))\n",
+        "density = 1000.0 * np.ones((Nx, Nz))\n",
+        "alpha_coeff = np.zeros((Nx, Nz))\n",
+        "\n",
+        "# non-dispersive\n",
+        "alpha_power = 2.0\n",
+        "\n",
+        "# cortical bone\n",
+        "sound_speed[:, 60:74] = 2800.0\n",
+        "density[:, 60:74] = 1850.0\n",
+        "alpha_coeff[:, 60:74] = 4.0\n",
+        "\n",
+        "c0_min = np.min(np.ravel(sound_speed))\n",
+        "c0_max = np.max(np.ravel(sound_speed))\n",
+        "\n",
+        "medium = kWaveMedium(sound_speed=sound_speed,\n",
+        "                     density=density,\n",
+        "                     alpha_coeff=alpha_coeff,\n",
+        "                     alpha_power=alpha_power)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "51b2b4e3-30dc-4a1b-aca5-0c9fe8dbe039",
+      "metadata": {
+        "id": "51b2b4e3-30dc-4a1b-aca5-0c9fe8dbe039"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE TRANSDUCER SETUP\n",
+        "# =========================================================================\n",
+        "\n",
+        "# radius of planar transducer\n",
+        "radius = 10E-3\n",
+        "\n",
+        "# frequency [Hz]\n",
+        "freq = 500e3\n",
+        "\n",
+        "# source pressure [Pa]\n",
+        "source_amp = np.array([60e3])\n",
+        "\n",
+        "# phase [rad]\n",
+        "source_phase = np.array([0.0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "5c64874d-5718-4f2d-ad47-954417ca9706",
+      "metadata": {
+        "id": "5c64874d-5718-4f2d-ad47-954417ca9706"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE COMPUTATIONAL PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "useMaxTimeStep: bool = True\n",
+        "\n",
+        "# wavelength\n",
+        "k_min: float = c0_min / freq\n",
+        "\n",
+        "# points per wavelength\n",
+        "ppw: float = k_min / dx\n",
+        "\n",
+        "# number of periods to record\n",
+        "record_periods: int = 3\n",
+        "\n",
+        "# compute points per period\n",
+        "ppp: int = 60\n",
+        "\n",
+        "# CFL number determines time step\n",
+        "cfl: float = (ppw / ppp)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "aaacacf8-cce0-4e7d-b76c-e87ad1236377",
+      "metadata": {
+        "id": "aaacacf8-cce0-4e7d-b76c-e87ad1236377"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE KGRID\n",
+        "# =========================================================================\n",
+        "\n",
+        "grid_size_points = Vector([Nx, Nz])\n",
+        "\n",
+        "grid_spacing_meters = Vector([dx, dz])\n",
+        "\n",
+        "# create the k-space grid\n",
+        "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "b181b1d1-b13f-40b2-9271-84e2d0658c55",
+      "metadata": {
+        "id": "b181b1d1-b13f-40b2-9271-84e2d0658c55"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE TIME VECTOR\n",
+        "# =========================================================================\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+        "\n",
+        "dt_stability_limit = check_stability(kgrid, medium)\n",
+        "\n",
+        "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):\n",
+        "    dt_old = dt\n",
+        "    ppp = np.ceil(1.0 / (dt_stability_limit * freq))\n",
+        "    dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# calculate the number of time steps to reach steady state\n",
+        "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+        "\n",
+        "# create the time array using an integer number of points per period\n",
+        "Nt = round(t_end / dt)\n",
+        "\n",
+        "# make time array\n",
+        "kgrid.setTime(Nt, dt)\n",
+        "\n",
+        "# calculate the actual CFL after adjusting for dt\n",
+        "cfl_actual = 1.0 / (dt * freq)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "871a4da9-e2c6-42e2-806c-ef5b7d0d83f6",
+      "metadata": {
+        "id": "871a4da9-e2c6-42e2-806c-ef5b7d0d83f6"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SOURCE PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "# create empty kWaveArray this specfies the transducer properties\n",
+        "karray = kWaveArray(bli_tolerance=0.01,\n",
+        "                    upsampling_rate=16,\n",
+        "                    single_precision=True)\n",
+        "\n",
+        "# element positions\n",
+        "start_point = [-radius, kgrid.y_vec[0].item()]\n",
+        "\n",
+        "end_point = [radius + dx, kgrid.y_vec[0].item()]\n",
+        "\n",
+        "karray.add_line_element(start_point, end_point)\n",
+        "\n",
+        "# create time varying source\n",
+        "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+        "                               freq,\n",
+        "                               source_amp,\n",
+        "                               source_phase)\n",
+        "\n",
+        "# make a source object.\n",
+        "source = kSource()\n",
+        "\n",
+        "# assign binary mask using the karray\n",
+        "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+        "\n",
+        "# assign source pressure output in time\n",
+        "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "1a163bb4-585b-4dbc-a3fe-eac35d355d34",
+      "metadata": {
+        "id": "1a163bb4-585b-4dbc-a3fe-eac35d355d34"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SENSOR PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "sensor = kSensor()\n",
+        "\n",
+        "# set sensor mask: the mask says at which points data should be recorded\n",
+        "sensor.mask = np.ones((Nx, Nz), dtype=bool)\n",
+        "\n",
+        "# set the record type: record the pressure waveform\n",
+        "sensor.record = ['p']\n",
+        "\n",
+        "# record the final few periods when the field is in steady state\n",
+        "sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "70482df0-7ccc-4e42-b00d-88e8952e28f5",
+      "metadata": {
+        "id": "70482df0-7ccc-4e42-b00d-88e8952e28f5"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SIMULATION PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "DATA_CAST = 'single'\n",
+        "DATA_PATH = './'\n",
+        "\n",
+        "input_filename = 'ph1_bm3_sc2_input.h5'\n",
+        "output_filename = 'ph1_bm3_sc2_output.h5'\n",
+        "\n",
+        "# options for writing to file, but not doing simulations\n",
+        "simulation_options = SimulationOptions(\n",
+        "    data_cast=DATA_CAST,\n",
+        "    data_recast=True,\n",
+        "    save_to_disk=True,\n",
+        "    input_filename=input_filename,\n",
+        "    output_filename=output_filename,\n",
+        "    save_to_disk_exit=False,\n",
+        "    data_path=DATA_PATH,\n",
+        "    pml_inside=False)\n",
+        "\n",
+        "execution_options = SimulationExecutionOptions(\n",
+        "    is_gpu_simulation=True,\n",
+        "    delete_data=False,\n",
+        "    verbose_level=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "3d698217-80ac-42b2-bf58-d0fb8632cb40",
+      "metadata": {
+        "id": "3d698217-80ac-42b2-bf58-d0fb8632cb40",
+        "outputId": "d92cd187-b30e-41bf-bb9d-fc995e83b9f9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Highest prime factors in each dimension are [181 281]\n",
+            "WARNING:root:Use dimension sizes with lower prime factors to improve speed\n",
+            "WARNING:root:DeprecationWarning: Attributes will soon be typed when saved and not saved \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "┌───────────────────────────────────────────────────────────────┐\n",
+            "│                  kspaceFirstOrder-CUDA v1.3                   │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Git hash:            468dc31c2842a7df5f2a07c3a13c16c9b0b2b770 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Reading simulation configuration:                        Done │\n",
+            "│ File format version:                                      1.2 │\n",
+            "│ Selected GPU device id:                                     0 │\n",
+            "│ GPU device name:                                     Tesla T4 │\n",
+            "│ Number of CPU threads:                                      2 │\n",
+            "│ Processor name:                Intel(R) Xeon(R) CPU @ 2.00GHz │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                      Simulation details                       │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Domain dimensions:                                  181 x 281 │\n",
+            "│ Medium type:                                               2D │\n",
+            "│ Simulation time steps:                                   5212 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Input file:  ./ph1_bm3_sc2_input.h5                           │\n",
+            "│ Input file:  ./ph1_bm3_sc2_output.h5                          │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Compression level:                                          0 │\n",
+            "│ Print progress interval:                                   5% │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Medium details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Wave propagation:                                      Linear │\n",
+            "│ Absorption type:                                    Power law │\n",
+            "│ Medium parameters:                              Heterogeneous │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Source details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure source:                                              │\n",
+            "│ + Source type:                                           Many │\n",
+            "│ + Source condition:                                  Additive │\n",
+            "│ + Memory usage:                                        2.11MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Sensor details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sensor mask type:                                       Index │\n",
+            "│ Sampling begins at time step:                            5033 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure sensor p_raw                                         │\n",
+            "│ + File usage:                                         23.33MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Initialization                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Memory allocation:                                       Done │\n",
+            "│ Data loading:                                                 │\n",
+            "│ + Reading input file:                                    Done │\n",
+            "│ + Creating output file:                                  Done │\n",
+            "│ Elapsed time:                                           0.01s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ FFT plans creation:                                      Done │\n",
+            "│ Pre-processing phase:                                    Done │\n",
+            "│ Elapsed time:                                           0.62s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                    Computational resources                    │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Current host memory in use:                             179MB │\n",
+            "│ Current device memory in use:                           123MB │\n",
+            "│ Expected output file size:                               23MB │\n",
+            "│ CUDA solver grid size [blocks x threads]:           199 x 256 │\n",
+            "│ CUDA sampler grid size [blocks x threads]:          133 x 256 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                          Simulation                           │\n",
+            "├──────────┬────────────────┬──────────────┬────────────────────┤\n",
+            "│ Progress │  Elapsed time  │  Time to go  │  Est. finish time  │\n",
+            "├──────────┼────────────────┼──────────────┼────────────────────┤\n",
+            "│     0%   │        0.001s  │      2.157s  │  13/03/24 10:56:27 │\n",
+            "│     5%   │        0.189s  │      3.571s  │  13/03/24 10:56:28 │\n",
+            "│    10%   │        0.326s  │      2.925s  │  13/03/24 10:56:27 │\n",
+            "│    15%   │        0.426s  │      2.412s  │  13/03/24 10:56:27 │\n",
+            "│    20%   │        0.519s  │      2.074s  │  13/03/24 10:56:27 │\n",
+            "│    25%   │        0.612s  │      1.833s  │  13/03/24 10:56:26 │\n",
+            "│    30%   │        0.705s  │      1.643s  │  13/03/24 10:56:26 │\n",
+            "│    35%   │        0.798s  │      1.479s  │  13/03/24 10:56:26 │\n",
+            "│    40%   │        0.890s  │      1.334s  │  13/03/24 10:56:26 │\n",
+            "│    45%   │        0.983s  │      1.200s  │  13/03/24 10:56:27 │\n",
+            "│    50%   │        1.076s  │      1.074s  │  13/03/24 10:56:27 │\n",
+            "│    55%   │        1.169s  │      0.955s  │  13/03/24 10:56:26 │\n",
+            "│    60%   │        1.267s  │      0.844s  │  13/03/24 10:56:26 │\n",
+            "│    65%   │        1.418s  │      0.763s  │  13/03/24 10:56:26 │\n",
+            "│    70%   │        1.621s  │      0.694s  │  13/03/24 10:56:26 │\n",
+            "│    75%   │        1.874s  │      0.623s  │  13/03/24 10:56:26 │\n",
+            "│    80%   │        2.119s  │      0.529s  │  13/03/24 10:56:27 │\n",
+            "│    85%   │        2.331s  │      0.410s  │  13/03/24 10:56:27 │\n",
+            "│    90%   │        2.526s  │      0.280s  │  13/03/24 10:56:27 │\n",
+            "│    95%   │        2.693s  │      0.141s  │  13/03/24 10:56:27 │\n",
+            "├──────────┴────────────────┴──────────────┴────────────────────┤\n",
+            "│ Elapsed time:                                           3.10s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sampled data post-processing:                            Done │\n",
+            "│ Elapsed time:                                           0.00s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                            Summary                            │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Peak host memory in use:                                179MB │\n",
+            "│ Peak device memory in use:                              123MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Total execution time:                                   4.18s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                       End of computation                      │\n",
+            "└───────────────────────────────────────────────────────────────┘\n"
+          ]
+        }
+      ],
+      "source": [
+        "# =========================================================================\n",
+        "# RUN THE SIMULATION\n",
+        "# =========================================================================\n",
+        "\n",
+        "sensor_data = kspace_first_order_2d_gpu(\n",
+        "    medium=medium,\n",
+        "    kgrid=kgrid,\n",
+        "    source=source,\n",
+        "    sensor=sensor,\n",
+        "    simulation_options=simulation_options,\n",
+        "    execution_options=execution_options)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "026657f3-96d8-44b2-aeed-b8ca241285d5",
+      "metadata": {
+        "id": "026657f3-96d8-44b2-aeed-b8ca241285d5"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# VISUALIZATION\n",
+        "# =========================================================================\n",
+        "\n",
+        "# sampling frequency\n",
+        "fs = 1.0 / kgrid.dt\n",
+        "\n",
+        "# get Fourier coefficients\n",
+        "amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')\n",
+        "\n",
+        "# reshape to array\n",
+        "p = np.reshape(amp, (Nx, Nz), order='F')\n",
+        "\n",
+        "# axes for plotting\n",
+        "x_vec = kgrid.x_vec\n",
+        "y_vec = kgrid.y_vec[0] - kgrid.y_vec"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig1, ax1 = plt.subplots(1, 1)\n",
+        "p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),\n",
+        "                    1e3 * np.squeeze(y_vec),\n",
+        "                    np.flip(p.T, axis=1) / 1e3,\n",
+        "                    shading='gouraud', cmap='viridis')\n",
+        "ax1.set(xlabel='Lateral Position [mm]',\n",
+        "        ylabel='Axial Position [mm]',\n",
+        "        title='PH1-BM3-SC2')\n",
+        "ax1.set_aspect('equal')\n",
+        "cbar1 = fig1.colorbar(p1, ax=ax1)\n",
+        "_ = cbar1.ax.set_title('[kPa]', fontsize='small')"
+      ],
+      "metadata": {
+        "id": "fAOFrBjOdbsn"
+      },
+      "id": "fAOFrBjOdbsn",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "dd4be3b3-5c8b-4b0c-aa08-5a3005af9572",
+      "metadata": {
+        "id": "dd4be3b3-5c8b-4b0c-aa08-5a3005af9572",
+        "outputId": "045607fe-db63-4639-f329-d682a87e1725",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig2, ax2 = plt.subplots(1, 1)\n",
+        "ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )\n",
+        "ax2.set(xlabel='Axial Position [mm]',\n",
+        "        ylabel='Pressure [kPa]',\n",
+        "        title='PH1-BM3-SC2')\n",
+        "ax2.grid(True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "82d8b1d8-b793-4202-87f9-bb7f9c1f3da8",
+      "metadata": {
+        "id": "82d8b1d8-b793-4202-87f9-bb7f9c1f3da8"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/benchmarks/3/ph1-bm3-sc2.py b/examples/benchmarks/3/ph1-bm3-sc2.py
new file mode 100644
index 000000000..874d4a468
--- /dev/null
+++ b/examples/benchmarks/3/ph1-bm3-sc2.py
@@ -0,0 +1,254 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+dx: float = 0.5e-3
+dz: float = dx
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+# cortical bone
+sound_speed[:, 60:74] = 2800.0
+density[:, 60:74] = 1850.0
+alpha_coeff[:, 60:74] = 4.0
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+radius: float = 10.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm3_sc2_input.h5'
+output_filename = 'ph1_bm3_sc2_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM3-SC2')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM3-SC2')
+ax2.grid(True)
+
+plt.show()
\ No newline at end of file
diff --git a/examples/benchmarks/4/ph1-bm4-sc1.ipynb b/examples/benchmarks/4/ph1-bm4-sc1.ipynb
new file mode 100644
index 000000000..940d1702c
--- /dev/null
+++ b/examples/benchmarks/4/ph1-bm4-sc1.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "20662708-c2b3-46e9-85fb-15270dff5f8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: k-wave-python in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.3.0)\n",
+      "Requirement already satisfied: deepdiff==6.3.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (6.3.1)\n",
+      "Requirement already satisfied: h5py==3.9.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (3.9.0)\n",
+      "Requirement already satisfied: matplotlib==3.7.2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (3.7.2)\n",
+      "Requirement already satisfied: numpy<1.25.0,>=1.22.2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (1.24.4)\n",
+      "Requirement already satisfied: opencv-python==4.8.0.76 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (4.8.0.76)\n",
+      "Requirement already satisfied: scikit-image==0.21.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (0.21.0)\n",
+      "Requirement already satisfied: scipy==1.10.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (1.10.1)\n",
+      "Requirement already satisfied: uff-reader==0.0.2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from k-wave-python) (0.0.2)\n",
+      "Requirement already satisfied: ordered-set<4.2.0,>=4.0.2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from deepdiff==6.3.1->k-wave-python) (4.1.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (4.47.2)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (23.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (10.2.0)\n",
+      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib==3.7.2->k-wave-python) (2.8.2)\n",
+      "Requirement already satisfied: networkx>=2.8 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-image==0.21.0->k-wave-python) (3.1)\n",
+      "Requirement already satisfied: imageio>=2.27 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-image==0.21.0->k-wave-python) (2.31.2)\n",
+      "Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-image==0.21.0->k-wave-python) (2023.8.25)\n",
+      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-image==0.21.0->k-wave-python) (1.4.1)\n",
+      "Requirement already satisfied: lazy_loader>=0.2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-image==0.21.0->k-wave-python) (0.3)\n",
+      "Requirement already satisfied: pytest>=6.2.4 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from uff-reader==0.0.2->k-wave-python) (7.4.4)\n",
+      "Requirement already satisfied: requests>=2.26.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from uff-reader==0.0.2->k-wave-python) (2.31.0)\n",
+      "Requirement already satisfied: iniconfig in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pytest>=6.2.4->uff-reader==0.0.2->k-wave-python) (2.0.0)\n",
+      "Requirement already satisfied: pluggy<2.0,>=0.12 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pytest>=6.2.4->uff-reader==0.0.2->k-wave-python) (1.3.0)\n",
+      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pytest>=6.2.4->uff-reader==0.0.2->k-wave-python) (1.1.3)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pytest>=6.2.4->uff-reader==0.0.2->k-wave-python) (2.0.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pytest>=6.2.4->uff-reader==0.0.2->k-wave-python) (0.4.6)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib==3.7.2->k-wave-python) (1.16.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.26.0->uff-reader==0.0.2->k-wave-python) (3.2.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.26.0->uff-reader==0.0.2->k-wave-python) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.26.0->uff-reader==0.0.2->k-wave-python) (2.0.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\dsinden\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.26.0->uff-reader==0.0.2->k-wave-python) (2023.7.22)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.3.2 -> 24.0\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install k-wave-python\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "from kwave.data import Vector\n",
+    "from kwave.utils.kwave_array import kWaveArray\n",
+    "from kwave.utils.checks import check_stability\n",
+    "from kwave.kgrid import kWaveGrid\n",
+    "from kwave.kmedium import kWaveMedium\n",
+    "from kwave.ksource import kSource\n",
+    "from kwave.ksensor import kSensor\n",
+    "from kwave.utils.signals import create_cw_signals\n",
+    "from kwave.utils.filters import extract_amp_phase\n",
+    "from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu\n",
+    "\n",
+    "from kwave.options.simulation_options import SimulationOptions\n",
+    "from kwave.options.simulation_execution_options import SimulationExecutionOptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4a9227e2-afab-4127-bd0b-cc37ab176e09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "useMaxTimeStep: bool = True\n",
+    "\n",
+    "Nx: int = 141\n",
+    "Nz: int = 241\n",
+    "\n",
+    "dx: float = 0.5e-3\n",
+    "dz: float = dx\n",
+    "\n",
+    "focus: int = 128\n",
+    "\n",
+    "focus_coords = [(Nx - 1) // 2, focus]\n",
+    "\n",
+    "bowl_coords = [(Nx - 1) // 2, 0]\n",
+    "\n",
+    "# =========================================================================\n",
+    "# DEFINE THE MATERIAL PROPERTIES\n",
+    "# ========================================================================="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bffb0a4a-8934-401a-9eda-5be224758f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# water\n",
+    "sound_speed = 1500.0 * np.ones((Nx, Nz))\n",
+    "density = 1000.0 * np.ones((Nx, Nz))\n",
+    "alpha_coeff = np.zeros((Nx, Nz))\n",
+    "\n",
+    "# non-dispersive\n",
+    "alpha_power = 2.0\n",
+    "\n",
+    "water_depth = 26.0 / 1000.0\n",
+    "skin_depth = 4.0 / 1000.0\n",
+    "outer_cortical_depth = 1.5 / 1000.0\n",
+    "trabecular_depth = 4.0 / 1000.0\n",
+    "inner_cortical_depth = 1.0 / 1000.0\n",
+    "\n",
+    "water: int = int(water_depth / dx)\n",
+    "skin: int = water + int(skin_depth / dx)\n",
+    "outer_cortical: int = skin + int(outer_cortical_depth / dx)\n",
+    "trabecular: int = outer_cortical + int(trabecular_depth / dx)\n",
+    "inner_cortical: int = trabecular + int(inner_cortical_depth / dx)\n",
+    "\n",
+    "# skin\n",
+    "sound_speed[:, water:skin] = 1610.0\n",
+    "density[:, water:skin] = 1090.0\n",
+    "alpha_coeff[:, water:skin] = 0.2\n",
+    "\n",
+    "# outer cortical bone\n",
+    "sound_speed[:, skin:outer_cortical] = 2800.0\n",
+    "density[:, skin:outer_cortical] = 1850.0\n",
+    "alpha_coeff[:, skin:outer_cortical] = 4.0\n",
+    "\n",
+    "# trabecular \n",
+    "sound_speed[:, outer_cortical:trabecular] = 2300.0\n",
+    "density[:, outer_cortical:trabecular] = 1700.0\n",
+    "alpha_coeff[:, outer_cortical:trabecular] = 8.0\n",
+    "\n",
+    "# inner cortical bone\n",
+    "sound_speed[:, trabecular:inner_cortical] = 2800.0\n",
+    "density[:, trabecular:inner_cortical] = 1850.0\n",
+    "alpha_coeff[:, trabecular:inner_cortical] = 4.0\n",
+    "\n",
+    "# brain\n",
+    "sound_speed[:, inner_cortical:-1] = 1560.0\n",
+    "density[:, inner_cortical:-1] = 1040.0\n",
+    "alpha_coeff[:, inner_cortical:-1] = 0.3\n",
+    "\n",
+    "c0_min = np.min(np.ravel(sound_speed))\n",
+    "c0_max = np.max(np.ravel(sound_speed))\n",
+    "\n",
+    "medium = kWaveMedium(sound_speed=sound_speed,\n",
+    "                     density=density,\n",
+    "                     alpha_coeff=alpha_coeff,\n",
+    "                     alpha_power=alpha_power)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5abae2d8-3f99-4c7d-9269-869d4556f47a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE TRANSDUCER SETUP\n",
+    "# =========================================================================\n",
+    "\n",
+    "# bowl radius of curvature [m]\n",
+    "source_roc: float = 64.0e-3\n",
+    "\n",
+    "# bowl openning [m]\n",
+    "diameters: float = 64.0e-3\n",
+    "\n",
+    "# frequency [Hz]\n",
+    "freq = 500e3\n",
+    "\n",
+    "# source pressure [Pa]\n",
+    "source_amp = np.array([60e3])\n",
+    "\n",
+    "# phase [rad]\n",
+    "source_phase = np.array([0.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6e784e8f-460f-4e6b-9049-e6b74310ca9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE COMPUTATIONAL PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "# wavelength\n",
+    "k_min: float = c0_min / freq\n",
+    "\n",
+    "# points per wavelength\n",
+    "ppw: float = k_min / dx\n",
+    "\n",
+    "# number of periods to record\n",
+    "record_periods: int = 3\n",
+    "\n",
+    "# compute points per period\n",
+    "ppp: int = 60\n",
+    "\n",
+    "# CFL number determines time step\n",
+    "cfl: float = (ppw / ppp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ded3a679-5954-455e-8e2f-22c4ffcef11c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE KGRID\n",
+    "# =========================================================================\n",
+    "\n",
+    "grid_size_points = Vector([Nx, Nz])\n",
+    "\n",
+    "grid_spacing_meters = Vector([dx, dz])\n",
+    "\n",
+    "# create the k-space grid\n",
+    "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "304001dd-3549-4582-a290-bd0a7e441ed1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE TIME VECTOR\n",
+    "# =========================================================================\n",
+    "\n",
+    "# compute corresponding time stepping\n",
+    "dt = 1.0 / (ppp * freq)\n",
+    "\n",
+    "# compute corresponding time stepping\n",
+    "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+    "\n",
+    "dt_stability_limit = check_stability(kgrid, medium)\n",
+    "\n",
+    "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):\n",
+    "    dt_old = dt\n",
+    "    ppp = np.ceil(1.0 / (dt_stability_limit * freq))\n",
+    "    dt = 1.0 / (ppp * freq)\n",
+    "\n",
+    "# calculate the number of time steps to reach steady state\n",
+    "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+    "\n",
+    "# create the time array using an integer number of points per period\n",
+    "Nt = round(t_end / dt)\n",
+    "\n",
+    "# make time array\n",
+    "kgrid.setTime(Nt, dt)\n",
+    "\n",
+    "# calculate the actual CFL after adjusting for dt\n",
+    "cfl_actual = 1.0 / (dt * freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c43e8cd0-1ae0-4c97-a219-0c686c488b00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "approximate size of source matrix: 106.75 MB (float32 precision)\n",
+      "total computation time: 1.10s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SOURCE PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "# create empty kWaveArray this specfies the transducer properties\n",
+    "karray = kWaveArray(bli_tolerance=0.01,\n",
+    "                    upsampling_rate=16,\n",
+    "                    single_precision=True)\n",
+    "\n",
+    "# set bowl position and orientation\n",
+    "bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),\n",
+    "            kgrid.y_vec[bowl_coords[1]].item()]\n",
+    "\n",
+    "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+    "             kgrid.y_vec[focus_coords[1]].item()]\n",
+    "\n",
+    "# add bowl shaped element\n",
+    "karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)\n",
+    "\n",
+    "# create time varying source\n",
+    "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+    "                               freq,\n",
+    "                               source_amp,\n",
+    "                               source_phase)\n",
+    "\n",
+    "# make a source object.\n",
+    "source = kSource()\n",
+    "\n",
+    "# assign binary mask using the karray\n",
+    "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+    "\n",
+    "# assign source pressure output in time\n",
+    "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "814c5a5e-2128-4a87-b2ba-e879c5f97bfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SENSOR PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "sensor = kSensor()\n",
+    "\n",
+    "# set sensor mask: the mask says at which points data should be recorded\n",
+    "sensor.mask = np.ones((Nx, Nz), dtype=bool)\n",
+    "\n",
+    "# set the record type: record the pressure waveform\n",
+    "sensor.record = ['p']\n",
+    "\n",
+    "# record the final few periods when the field is in steady state\n",
+    "sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8c345027-8983-4a99-a4ef-dc194cbfa415",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running k-Wave simulation...\n",
+      "  start time: 17-Mar-2024-21-58-55\n",
+      "set_flags_from_list\n",
+      "cool\n",
+      "  dt: 17.857143ns, t_end: 93.053571us, time steps: 5212\n",
+      "  input grid size: 141 by 241 grid points (70.5mm by 120.5mm)\n",
+      "  maximum supported frequency: 1.489362MHz by 1.493776MHz\n",
+      "  expanding computational grid...\n",
+      "  computational grid size: 181 by 281 grid points\n",
+      "0\n",
+      "1\n",
+      "  precomputation completed in 0.169051s\n",
+      "  saving input files to disk...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dsinden\\Documents\\GitLab\\k-wave-python\\kwave\\kWaveSimulation.py:1323: UserWarning: WARNING: Highest prime factors in each dimension are [181 281]\n",
+      "  warn(f'WARNING: Highest prime factors in each dimension are {prime_facs}')\n",
+      "C:\\Users\\dsinden\\Documents\\GitLab\\k-wave-python\\kwave\\kWaveSimulation.py:1324: UserWarning: Use dimension sizes with lower prime factors to improve speed\n",
+      "  warn('Use dimension sizes with lower prime factors to improve speed')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  completed in 0.9901437s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# DEFINE THE SIMULATION PARAMETERS\n",
+    "# =========================================================================\n",
+    "\n",
+    "DATA_CAST = 'single'\n",
+    "DATA_PATH = './'\n",
+    "\n",
+    "input_filename = 'ph1_bm4_sc1_input.h5'\n",
+    "output_filename = 'ph1_bm4_sc1_output.h5'\n",
+    "\n",
+    "# options for writing to file, but not doing simulations\n",
+    "simulation_options = SimulationOptions(\n",
+    "    data_cast=DATA_CAST,\n",
+    "    data_recast=True,\n",
+    "    save_to_disk=True,\n",
+    "    input_filename=input_filename,\n",
+    "    output_filename=output_filename,\n",
+    "    save_to_disk_exit=False,\n",
+    "    data_path=DATA_PATH,\n",
+    "    pml_inside=False)\n",
+    "\n",
+    "execution_options = SimulationExecutionOptions(\n",
+    "    is_gpu_simulation=True,\n",
+    "    delete_data=False,\n",
+    "    verbose_level=2)\n",
+    "\n",
+    "sensor_data = kspace_first_order_2d_gpu(\n",
+    "    medium=medium,\n",
+    "    kgrid=kgrid,\n",
+    "    source=source,\n",
+    "    sensor=sensor,\n",
+    "    simulation_options=simulation_options,\n",
+    "    execution_options=execution_options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8164b2ca-efe1-4f33-a83d-9cfcc5ad6aff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# VISUALIZATION\n",
+    "# =========================================================================\n",
+    "\n",
+    "# sampling frequency\n",
+    "fs = 1.0 / kgrid.dt\n",
+    "\n",
+    "# get Fourier coefficients\n",
+    "amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,\n",
+    "                              window='Rectangular')\n",
+    "\n",
+    "# reshape to array\n",
+    "p = np.reshape(amp, (Nx, Nz), order='F')\n",
+    "\n",
+    "# axes for plotting\n",
+    "x_vec = kgrid.x_vec\n",
+    "y_vec = kgrid.y_vec[0] - kgrid.y_vec\n",
+    "\n",
+    "fig0, ax0 = plt.subplots(1, 1)\n",
+    "values = np.unique(sound_speed.ravel())\n",
+    "# get the colors of the values, according to the colormap used by imshow\n",
+    "colors = {1500.0: mcolors.to_rgb('#FFFFFF'),\n",
+    "          1610.0: mcolors.to_rgb('#FFC0CB'),\n",
+    "          1560.0: mcolors.to_rgb('#FFFF00'),\n",
+    "          2800.0: mcolors.to_rgb('#808080'),\n",
+    "          2300.0: mcolors.to_rgb('#C0C0C0')}\n",
+    "labels = {1500.0: 'water',\n",
+    "          1610.0: 'skin',\n",
+    "          1560.0: 'brain',\n",
+    "          2800.0: 'cortical',\n",
+    "          2300.0: 'trabecular'}\n",
+    "# create a patch (proxy artist) for every color \n",
+    "patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]\n",
+    "# put those patched as legend-handles into the legend\n",
+    "ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n",
+    "sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  \n",
+    "im = ax0.imshow(sos, interpolation='none')\n",
+    "ax0.grid(False)\n",
+    "ax0.set_aspect('equal')\n",
+    "\n",
+    "# get grid weights\n",
+    "grid_weights = karray.get_array_grid_weights(kgrid)\n",
+    "fig1, (ax1a, ax1b) = plt.subplots(1, 2)\n",
+    "#(1e3 * y_vec, 1e3 * x_vec, np.flip(source.p_mask, axis=0), shading='nearest')\n",
+    "ax1a.imshow(source.p_mask[:, :].T)\n",
+    "ax1a.set(xlabel='y [mm]', ylabel='x [mm]', title='Source Mask')\n",
+    "ax1b.imshow(grid_weights.T)\n",
+    "_ = ax1b.set_title('Off-Grid Source Weights')\n",
+    "plt.tight_layout(pad=1.2)\n",
+    "\n",
+    "fig2, ax2 = plt.subplots(1, 1)\n",
+    "p2 = ax2.pcolormesh(1e3 * np.squeeze(x_vec),\n",
+    "                    1e3 * np.squeeze(y_vec),\n",
+    "                    np.flip(p.T, axis=1) / 1e3,\n",
+    "                    shading='gouraud', cmap='viridis')\n",
+    "ax2.set(xlabel='Lateral Position [mm]', ylabel='Axial Position [mm]', title='PH1-BM4-SC1')\n",
+    "ax2.set_aspect('equal')\n",
+    "cbar2 = fig2.colorbar(p2, ax=ax2)\n",
+    "_ = cbar2.ax.set_title('[kPa]', fontsize='small')\n",
+    "\n",
+    "fig3, ax3 = plt.subplots(1, 1)\n",
+    "ax3.plot(-1e3 * y_vec, p[(Nx - 1) // 2, :] / 1e3)\n",
+    "ax3.set(xlabel='Axial Position [mm]', ylabel='Pressure [kPa]', title='PH1-BM4-SC1')\n",
+    "ax3.grid(True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5a9a9c9-c96b-44b8-a8ba-feb43a93cc55",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27e55727-adcc-4d64-a365-cd9d84ce3780",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/benchmarks/4/ph1-bm4-sc1.py b/examples/benchmarks/4/ph1-bm4-sc1.py
new file mode 100644
index 000000000..9105515b7
--- /dev/null
+++ b/examples/benchmarks/4/ph1-bm4-sc1.py
@@ -0,0 +1,326 @@
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [(Nx - 1) // 2, focus]
+
+bowl_coords = [(Nx - 1) // 2, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth = 26.0 / 1000.0
+skin_depth = 4.0 / 1000.0
+outer_cortical_depth = 1.5 / 1000.0
+trabecular_depth = 4.0 / 1000.0
+inner_cortical_depth = 1.0 / 1000.0
+
+water: int = int(water_depth / dx)
+skin: int = water + int(skin_depth / dx)
+outer_cortical: int = skin + int(outer_cortical_depth / dx)
+trabecular: int = outer_cortical + int(trabecular_depth / dx)
+inner_cortical: int = trabecular + int(inner_cortical_depth / dx)
+
+# skin
+sound_speed[:, water:skin] = 1610.0
+density[:, water:skin] = 1090.0
+alpha_coeff[:, water:skin] = 0.2
+
+# outer cortical bone
+sound_speed[:, skin:outer_cortical] = 2800.0
+density[:, skin:outer_cortical] = 1850.0
+alpha_coeff[:, skin:outer_cortical] = 4.0
+
+# trabecular 
+sound_speed[:, outer_cortical:trabecular] = 2300.0
+density[:, outer_cortical:trabecular] = 1700.0
+alpha_coeff[:, outer_cortical:trabecular] = 8.0
+
+# inner cortical bone
+sound_speed[:, trabecular:inner_cortical] = 2800.0
+density[:, trabecular:inner_cortical] = 1850.0
+alpha_coeff[:, trabecular:inner_cortical] = 4.0
+
+# brain
+sound_speed[:, inner_cortical:-1] = 1560.0
+density[:, inner_cortical:-1] = 1040.0
+alpha_coeff[:, inner_cortical:-1] = 0.3
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# bowl openning [m]
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm4_sc1_input.h5'
+output_filename = 'ph1_bm4_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'trabecular',
+          2800.0: 'cortical',
+          2300.0: 'brain'}
+
+print(labels)
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM4-SC1')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM4-SC1')
+ax2.grid(True)
+
+plt.show()
\ No newline at end of file
diff --git a/examples/benchmarks/4/ph1-bm4-sc2.py b/examples/benchmarks/4/ph1-bm4-sc2.py
new file mode 100644
index 000000000..a7ff8f63d
--- /dev/null
+++ b/examples/benchmarks/4/ph1-bm4-sc2.py
@@ -0,0 +1,316 @@
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+dx: float = 0.5e-3
+dz: float = dx
+
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth = 26.0 / 1000.0
+skin_depth = 4.0 / 1000.0
+outer_cortical_depth = 1.5 / 1000.0
+trabecular_depth = 4.0 / 1000.0
+inner_cortical_depth = 1.0 / 1000.0
+
+water: int = int(water_depth / dx)
+skin: int = water + int(skin_depth / dx)
+outer_cortical: int = skin + int(outer_cortical_depth / dx)
+trabecular: int = outer_cortical + int(trabecular_depth / dx)
+inner_cortical: int = trabecular + int(inner_cortical_depth / dx)
+
+# skin
+sound_speed[:, water:skin] = 1610.0
+density[:, water:skin] = 1090.0
+alpha_coeff[:, water:skin] = 0.2
+
+# outer cortical bone
+sound_speed[:, skin:outer_cortical] = 2800.0
+density[:, skin:outer_cortical] = 1850.0
+alpha_coeff[:, skin:outer_cortical] = 4.0
+
+# trabecular 
+sound_speed[:, outer_cortical:trabecular] = 2300.0
+density[:, outer_cortical:trabecular] = 1700.0
+alpha_coeff[:, outer_cortical:trabecular] = 8.0
+
+# inner cortical bone
+sound_speed[:, trabecular:inner_cortical] = 2800.0
+density[:, trabecular:inner_cortical] = 1850.0
+alpha_coeff[:, trabecular:inner_cortical] = 4.0
+
+# brain
+sound_speed[:, inner_cortical:-1] = 1560.0
+density[:, inner_cortical:-1] = 1040.0
+alpha_coeff[:, inner_cortical:-1] = 0.3
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# 
+radius: float = 10.0E-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# positions of planar element
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+# add element
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm4_sc2_input.h5'
+output_filename = 'ph1_bm4_sc2_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'trabecular',
+          2800.0: 'cortical',
+          2300.0: 'brain'}
+
+print(labels)
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM4-SC2')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM4-SC2')
+ax2.grid(True)
+
+plt.show()
\ No newline at end of file
diff --git a/examples/benchmarks/5/ph1-bm5-sc1.py b/examples/benchmarks/5/ph1-bm5-sc1.py
new file mode 100644
index 000000000..6e787a285
--- /dev/null
+++ b/examples/benchmarks/5/ph1-bm5-sc1.py
@@ -0,0 +1,336 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.utils.mapgen import make_arc
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [centre, focus]
+
+bowl_coords = [centre, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth: float = 30.0 / 1000.0
+
+cortical_depth: float = 6.5 / 1000.0
+
+outer_cortical_roc: float = 75.0 / 1000.0
+
+inner_cortical_roc: float = outer_cortical_roc - cortical_depth
+
+water: int = int(water_depth / dx)
+cortical: int = water + int(cortical_depth / dx)
+outer_cortical: int = water + int(outer_cortical_roc / dx)
+inner_cortical: int = water + int(inner_cortical_roc / dx)
+
+outer_arc = make_arc(Vector([Nx, Nz]), 
+                        np.array([centre, water]), 
+                        int(outer_cortical_roc / dx), 
+                        Nx, 
+                        Vector([centre, outer_cortical]))
+
+inner_arc = make_arc(Vector([Nx, Nz]), 
+                        np.array([centre, cortical]), 
+                        int(inner_cortical_roc / dx), 
+                        Nx, 
+                        Vector([centre, inner_cortical]))
+
+cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+for i in range(Nx):
+  inner_boundary = False
+  outer_boundary = False
+  for j in range(Nz):
+    if inner_arc[i, j]:
+      inner_boundary = True
+    if outer_arc[i, j]:
+      outer_boundary = True
+    if (outer_boundary and not inner_boundary):
+      cortical_mask[i, j] = True
+
+# outer cortical bone
+sound_speed[cortical_mask] = 2800.0
+density[cortical_mask] = 1850.0
+alpha_coeff[cortical_mask] = 4.0
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# bowl openning [m]
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm5_sc1_input.h5'
+output_filename = 'ph1_bm5_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'trabecular',
+          2800.0: 'cortical',
+          2300.0: 'brain'}
+
+print(labels)
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM5-SC1')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM5-SC1')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/5/ph1-bm5-sc2.py b/examples/benchmarks/5/ph1-bm5-sc2.py
new file mode 100644
index 000000000..a534ab0a8
--- /dev/null
+++ b/examples/benchmarks/5/ph1-bm5-sc2.py
@@ -0,0 +1,325 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.utils.mapgen import make_arc
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth: float = 30.0 / 1000.0
+
+cortical_depth: float = 6.5 / 1000.0
+
+outer_cortical_roc: float = 75.0 / 1000.0
+
+inner_cortical_roc: float = outer_cortical_roc - cortical_depth
+
+water: int = int(water_depth / dx)
+cortical: int = water + int(cortical_depth / dx)
+outer_cortical: int = water + int(outer_cortical_roc / dx)
+inner_cortical: int = water + int(inner_cortical_roc / dx)
+
+outer_arc = make_arc(Vector([Nx, Nz]), 
+                        np.array([centre, water]), 
+                        int(outer_cortical_roc / dx), 
+                        Nx, 
+                        Vector([centre, outer_cortical]))
+
+inner_arc = make_arc(Vector([Nx, Nz]), 
+                        np.array([centre, cortical]), 
+                        int(inner_cortical_roc / dx), 
+                        Nx, 
+                        Vector([centre, inner_cortical]))
+
+cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+for i in range(Nx):
+  inner_boundary = False
+  outer_boundary = False
+  for j in range(Nz):
+    if inner_arc[i, j]:
+      inner_boundary = True
+    if outer_arc[i, j]:
+      outer_boundary = True
+    if (outer_boundary and not inner_boundary):
+      cortical_mask[i, j] = True
+
+# outer cortical bone
+sound_speed[cortical_mask] = 2800.0
+density[cortical_mask] = 1850.0
+alpha_coeff[cortical_mask] = 4.0
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# radius of planar element
+radius: float = 10.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 60
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# position of planar element
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+# add element
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm5_sc2_input.h5'
+output_filename = 'ph1_bm5_sc2_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'trabecular',
+          2800.0: 'cortical',
+          2300.0: 'brain'}
+
+print(labels)
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+p1 = ax1.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax1.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM5-SC2')
+ax1.set_aspect('equal')
+cbar1 = fig1.colorbar(p1, ax=ax1)
+_ = cbar1.ax.set_title('[kPa]', fontsize='small')
+
+fig2, ax2 = plt.subplots(1, 1)
+ax2.plot(-1e3 * y_vec, p[(Nx-1)//2, :] / 1e3 )
+ax2.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM5-SC2')
+ax2.grid(True)
+
+plt.show()
diff --git a/examples/benchmarks/6/ph1-bm6-sc1.py b/examples/benchmarks/6/ph1-bm6-sc1.py
new file mode 100644
index 000000000..6846af17b
--- /dev/null
+++ b/examples/benchmarks/6/ph1-bm6-sc1.py
@@ -0,0 +1,371 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.utils.mapgen import make_arc
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+focus: int = 128
+
+focus_coords = [centre, focus]
+
+bowl_coords = [centre, 0]
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth: float = 26.0 / 1000.0
+skin_depth: float = 4.0 / 1000.0
+outer_cortical_depth: float = 1.5 / 1000.0
+trabecular_depth: float = 4.0 / 1000.0
+inner_cortical_depth: float = 1.0 / 1000.0
+
+outer_cortical_roc: float = 75.0 / 1000.0
+
+skull_centre: int = int(105.0e-3 / dx)
+
+skin_roc: float = outer_cortical_roc + skin_depth
+trabecular_roc: float = outer_cortical_roc - outer_cortical_depth
+inner_cortical_roc: float = trabecular_roc - trabecular_depth
+brain_roc: float = inner_cortical_roc - inner_cortical_depth
+
+water: int = int(water_depth / dx)
+skin: int = water + int(skin_depth / dx)
+outer_cortical: int = skin + int(outer_cortical_depth / dx)
+trabecular: int = outer_cortical + int(trabecular_depth / dx)
+inner_cortical: int = trabecular + int(inner_cortical_depth / dx)
+
+skin_arc = make_arc(Vector([Nx, Nz]), # grid_size
+                    np.array([centre, water]), # arc_pos
+                    int(skin_roc / dx), # roc
+                    Nx, # width
+                    Vector([centre, skull_centre])) # focus_pos
+
+outer_cortical_arc = make_arc(Vector([Nx, Nz]), 
+                              np.array([centre, skin]), 
+                              int(outer_cortical_roc / dx), 
+                              Nx, 
+                              Vector([centre, skull_centre]))
+
+trabecular_arc = make_arc(Vector([Nx, Nz]), 
+                          np.array([centre, outer_cortical]), 
+                          int(trabecular_roc / dx), 
+                          Nx, 
+                          Vector([centre, skull_centre]))
+
+inner_cortical_arc = make_arc(Vector([Nx, Nz]), 
+                              np.array([centre, trabecular]), 
+                              int(inner_cortical_roc / dx), 
+                              Nx, 
+                              Vector([centre, skull_centre]))
+
+brain_arc = make_arc(Vector([Nx, Nz]), 
+                     np.array([centre, inner_cortical]), 
+                     int(brain_roc / dx), 
+                     Nx, 
+                     Vector([centre, skull_centre]))
+
+skin_mask = np.zeros((Nx, Nz), dtype=bool)
+cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+trabecular_mask = np.zeros((Nx, Nz), dtype=bool)
+inner_cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+brain_mask = np.zeros((Nx, Nz), dtype=bool)
+
+for i in range(Nx):
+  skin_boundary = False
+  inner_cortical_boundary = False
+  trabecular_boundary = False
+  outer_cortical_boundary = False
+  brain_boundary = False
+  for j in range(Nz):
+    # set boundaries
+    if skin_arc[i, j]:
+      skin_boundary = True
+    if outer_cortical_arc[i, j]:
+      outer_cortical_boundary = True
+    if trabecular_arc[i, j]:
+      trabecular_boundary = True
+    if inner_cortical_arc[i, j]:
+      inner_cortical_boundary = True
+    if brain_arc[i, j]:
+      brain_boundary = True
+    # set masks  
+    if (skin_boundary and not outer_cortical_boundary):
+      skin_mask[i, j] = True
+    if (outer_cortical_boundary and not trabecular_boundary):
+      cortical_mask[i, j] = True 
+    if (trabecular_boundary and not inner_cortical_boundary):
+      trabecular_mask[i, j] = True 
+    if (inner_cortical_boundary and not brain_boundary):
+      cortical_mask[i, j] = True 
+    if brain_boundary:
+      brain_mask[i, j] = True
+
+# skin
+sound_speed[skin_mask] = 1610.0
+density[skin_mask] = 1090.0
+alpha_coeff[skin_mask] = 0.2
+
+# cortical bone
+sound_speed[cortical_mask] = 2800.0
+density[cortical_mask] = 1850.0
+alpha_coeff[cortical_mask] = 4.0
+
+# trabecular 
+sound_speed[trabecular_mask] = 2300.0
+density[trabecular_mask] = 1700.0
+alpha_coeff[trabecular_mask] = 8.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item()]
+
+# add bowl shaped element
+karray.add_arc_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm6_sc1_input.h5'
+output_filename = 'ph1_bm6_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'brain',
+          2800.0: 'cortical',
+          2300.0: 'trabecular'}
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+ax1.imshow(skin_arc.T, alpha=0.5)
+ax1.imshow(outer_cortical_arc.T, alpha=0.5)
+ax1.imshow(trabecular_arc.T, alpha=0.5)
+ax1.imshow(inner_cortical_arc.T, alpha=0.5)
+ax1.imshow(brain_arc.T, alpha=0.5)
+
+fig2, ax2 = plt.subplots(1, 1)
+p2 = ax2.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax2.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM1-SC1')
+ax2.set_aspect('equal')
+cbar2 = fig2.colorbar(p2, ax=ax2)
+_ = cbar2.ax.set_title('[kPa]', fontsize='small')
+
+fig3, ax3 = plt.subplots(1, 1)
+ax3.plot(-1e3 * y_vec, p[(Nx - 1) // 2, :] / 1e3 )
+ax3.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM6-SC1')
+ax3.grid(True)
+
+
+plt.show()
diff --git a/examples/benchmarks/6/ph1-bm6-sc2.py b/examples/benchmarks/6/ph1-bm6-sc2.py
new file mode 100644
index 000000000..50765fc8c
--- /dev/null
+++ b/examples/benchmarks/6/ph1-bm6-sc2.py
@@ -0,0 +1,365 @@
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.colors as mcolors
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.utils.mapgen import make_arc
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder2D import kspace_first_order_2d_gpu
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+useMaxTimeStep: bool = True
+
+Nx: int = 141
+Nz: int = 241
+
+centre: int = (Nx - 1) // 2
+
+dx: float = 0.5e-3
+dz: float = dx
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones((Nx, Nz))
+density = 1000.0 * np.ones((Nx, Nz))
+alpha_coeff = np.zeros((Nx, Nz))
+
+# non-dispersive
+alpha_power = 2.0
+
+water_depth: float = 26.0 / 1000.0
+skin_depth: float = 4.0 / 1000.0
+outer_cortical_depth: float = 1.5 / 1000.0
+trabecular_depth: float = 4.0 / 1000.0
+inner_cortical_depth: float = 1.0 / 1000.0
+
+outer_cortical_roc: float = 75.0 / 1000.0
+
+skull_centre: int = int(105.0e-3 / dx)
+
+skin_roc: float = outer_cortical_roc + skin_depth
+trabecular_roc: float = outer_cortical_roc - outer_cortical_depth
+inner_cortical_roc: float = trabecular_roc - trabecular_depth
+brain_roc: float = inner_cortical_roc - inner_cortical_depth
+
+water: int = int(water_depth / dx)
+skin: int = water + int(skin_depth / dx)
+outer_cortical: int = skin + int(outer_cortical_depth / dx)
+trabecular: int = outer_cortical + int(trabecular_depth / dx)
+inner_cortical: int = trabecular + int(inner_cortical_depth / dx)
+
+skin_arc = make_arc(Vector([Nx, Nz]), # grid_size
+                    np.array([centre, water]), # arc_pos
+                    int(skin_roc / dx), # roc
+                    Nx, # width
+                    Vector([centre, skull_centre])) # focus_pos
+
+outer_cortical_arc = make_arc(Vector([Nx, Nz]), 
+                              np.array([centre, skin]), 
+                              int(outer_cortical_roc / dx), 
+                              Nx, 
+                              Vector([centre, skull_centre]))
+
+trabecular_arc = make_arc(Vector([Nx, Nz]), 
+                          np.array([centre, outer_cortical]), 
+                          int(trabecular_roc / dx), 
+                          Nx, 
+                          Vector([centre, skull_centre]))
+
+inner_cortical_arc = make_arc(Vector([Nx, Nz]), 
+                              np.array([centre, trabecular]), 
+                              int(inner_cortical_roc / dx), 
+                              Nx, 
+                              Vector([centre, skull_centre]))
+
+brain_arc = make_arc(Vector([Nx, Nz]), 
+                     np.array([centre, inner_cortical]), 
+                     int(brain_roc / dx), 
+                     Nx, 
+                     Vector([centre, skull_centre]))
+
+skin_mask = np.zeros((Nx, Nz), dtype=bool)
+cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+trabecular_mask = np.zeros((Nx, Nz), dtype=bool)
+inner_cortical_mask = np.zeros((Nx, Nz), dtype=bool)
+brain_mask = np.zeros((Nx, Nz), dtype=bool)
+
+for i in range(Nx):
+  skin_boundary = False
+  inner_cortical_boundary = False
+  trabecular_boundary = False
+  outer_cortical_boundary = False
+  brain_boundary = False
+  for j in range(Nz):
+    # set boundaries
+    if skin_arc[i, j]:
+      skin_boundary = True
+    if outer_cortical_arc[i, j]:
+      outer_cortical_boundary = True
+    if trabecular_arc[i, j]:
+      trabecular_boundary = True
+    if inner_cortical_arc[i, j]:
+      inner_cortical_boundary = True
+    if brain_arc[i, j]:
+      brain_boundary = True
+    # set masks  
+    if (skin_boundary and not outer_cortical_boundary):
+      skin_mask[i, j] = True
+    if (outer_cortical_boundary and not trabecular_boundary):
+      cortical_mask[i, j] = True 
+    if (trabecular_boundary and not inner_cortical_boundary):
+      trabecular_mask[i, j] = True 
+    if (inner_cortical_boundary and not brain_boundary):
+      cortical_mask[i, j] = True 
+    if brain_boundary:
+      brain_mask[i, j] = True
+
+# skin
+sound_speed[skin_mask] = 1610.0
+density[skin_mask] = 1090.0
+alpha_coeff[skin_mask] = 0.2
+
+# cortical bone
+sound_speed[cortical_mask] = 2800.0
+density[cortical_mask] = 1850.0
+alpha_coeff[cortical_mask] = 4.0
+
+# trabecular 
+sound_speed[trabecular_mask] = 2300.0
+density[trabecular_mask] = 1700.0
+alpha_coeff[trabecular_mask] = 8.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(np.ravel(sound_speed))
+c0_max = np.max(np.ravel(sound_speed))
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Nz])
+
+grid_spacing_meters = Vector([dx, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+#  radius of planar element
+radius: float = 10e-3 
+
+# position of planar element
+start_point = [-radius, kgrid.y_vec[0].item()]
+end_point = [radius + dx, kgrid.y_vec[0].item()]
+
+# add element
+karray.add_line_element(start_point, end_point)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = 'ph1_bm6_sc1_input.h5'
+output_filename = 'ph1_bm6_sc1_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspace_first_order_2d_gpu(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# VISUALIZATION
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1,
+                              window='Rectangular')
+
+# reshape to array
+p = np.reshape(amp, (Nx, Nz), order='F')
+
+# axes for plotting
+x_vec = kgrid.x_vec
+y_vec = kgrid.y_vec[0] - kgrid.y_vec
+
+fig0, ax0 = plt.subplots(1, 1)
+values = np.unique(sound_speed.ravel())
+# get the colors of the values, according to the colormap used by imshow
+colors = {1500.0: mcolors.to_rgb('#FFFFFF'),
+          1610.0: mcolors.to_rgb('#FFC0CB'),
+          1560.0: mcolors.to_rgb('#FFFF00'),
+          2800.0: mcolors.to_rgb('#808080'),
+          2300.0: mcolors.to_rgb('#C0C0C0')}
+labels = {1500.0: 'water',
+          1610.0: 'skin',
+          1560.0: 'brain',
+          2800.0: 'cortical',
+          2300.0: 'trabecular'}
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+# create a patch (proxy artist) for every color 
+patches = [mpatches.Patch(color=colors[i], label=labels[i]) for i in colors]
+# put those patched as legend-handles into the legend
+ax0.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
+sos = np.array([[colors[i] for i in j] for j in sound_speed.T])  
+im = ax0.imshow(sos, interpolation='none')
+ax0.grid(False)
+ax0.set_aspect('equal')
+
+fig1, ax1 = plt.subplots(1, 1)
+ax1.imshow(skin_arc.T, alpha=0.5)
+ax1.imshow(outer_cortical_arc.T, alpha=0.5)
+ax1.imshow(trabecular_arc.T, alpha=0.5)
+ax1.imshow(inner_cortical_arc.T, alpha=0.5)
+ax1.imshow(brain_arc.T, alpha=0.5)
+
+fig2, ax2 = plt.subplots(1, 1)
+p2 = ax2.pcolormesh(1e3 * np.squeeze(x_vec),
+                    1e3 * np.squeeze(y_vec),
+                    np.flip(p.T, axis=1) / 1e3,
+                    shading='gouraud', cmap='viridis')
+ax2.set(xlabel='Lateral Position [mm]',
+        ylabel='Axial Position [mm]',
+        title='PH1-BM1-SC1')
+ax2.set_aspect('equal')
+cbar2 = fig2.colorbar(p2, ax=ax2)
+_ = cbar2.ax.set_title('[kPa]', fontsize='small')
+
+fig3, ax3 = plt.subplots(1, 1)
+ax3.plot(-1e3 * y_vec, p[(Nx - 1) // 2, :] / 1e3 )
+ax3.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [kPa]',
+        title='PH1-BM6-SC1')
+ax3.grid(True)
+
+
+plt.show()
diff --git a/examples/benchmarks/7/ph1-bm7-sc1.ipynb b/examples/benchmarks/7/ph1-bm7-sc1.ipynb
new file mode 100644
index 000000000..7b2f3ba5c
--- /dev/null
+++ b/examples/benchmarks/7/ph1-bm7-sc1.ipynb
@@ -0,0 +1,937 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0a52fde0-1cee-4c8f-8d1d-5a860629ad19",
+      "metadata": {
+        "id": "0a52fde0-1cee-4c8f-8d1d-5a860629ad19"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install git+https://github.com/waltsims/k-wave-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from copy import deepcopy\n",
+        "import requests\n",
+        "import shutil\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "\n",
+        "from matplotlib.colors import Normalize\n",
+        "import matplotlib.cm as cm\n",
+        "\n",
+        "from cycler import cycler\n",
+        "\n",
+        "import h5py\n",
+        "\n",
+        "from skimage import measure\n",
+        "from skimage.segmentation import find_boundaries\n",
+        "from scipy.interpolate import interpn\n",
+        "\n",
+        "from kwave.data import Vector\n",
+        "from kwave.utils.kwave_array import kWaveArray\n",
+        "from kwave.utils.checks import check_stability\n",
+        "from kwave.kgrid import kWaveGrid\n",
+        "from kwave.kmedium import kWaveMedium\n",
+        "from kwave.ksource import kSource\n",
+        "from kwave.ksensor import kSensor\n",
+        "from kwave.utils.signals import create_cw_signals\n",
+        "from kwave.utils.filters import extract_amp_phase\n",
+        "from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG\n",
+        "\n",
+        "from kwave.options.simulation_options import SimulationOptions\n",
+        "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+        "\n",
+        "verbose: bool = True\n",
+        "savePlotting: bool = False\n",
+        "useMaxTimeStep: bool = True\n",
+        "\n",
+        "tag = 'bm7'\n",
+        "res = '1mm'\n",
+        "\n",
+        "mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'\n",
+        "\n",
+        "url = 'https://raw.githubusercontent.com/djps/k-wave-python/benchmarks/examples/benchmarks/7/skull_mask_bm7_dx_1mm.mat'\n",
+        "mask_filename = requests.get(url, stream=True)\n",
+        "mask_filename.raw.decode_content = True\n",
+        "with open(\"temp.h5\", \"wb\") as _fh:\n",
+        "    shutil.copyfileobj(mask_filename.raw, _fh)\n",
+        "data = h5py.File(\"temp.h5\", mode='r')\n",
+        "\n",
+        "# is given in millimetres\n",
+        "dx = data['dx'][:].item()\n",
+        "\n",
+        "# scale to metres\n",
+        "dx = dx / 1000.0\n",
+        "dy = dx\n",
+        "dz = dx\n",
+        "\n",
+        "xi = np.squeeze(np.asarray(data['xi'][:]))\n",
+        "yi = np.squeeze(np.asarray(data['yi'][:]))\n",
+        "zi = np.squeeze(np.asarray(data['zi'][:]))\n",
+        "\n",
+        "matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]\n",
+        "\n",
+        "skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)\n",
+        "brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)\n",
+        "\n",
+        "skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')\n",
+        "brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')\n",
+        "\n",
+        "water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))\n",
+        "water_mask = water_mask.astype(bool)\n",
+        "\n",
+        "skull_mask = np.swapaxes(skull_mask, 0, 2)\n",
+        "brain_mask = np.swapaxes(brain_mask, 0, 2)\n",
+        "water_mask = np.swapaxes(water_mask, 0, 2)\n",
+        "\n",
+        "Nx, Ny, Nz = skull_mask.shape\n",
+        "\n",
+        "focus = int(64 / data['dx'][:].item())\n",
+        "\n",
+        "focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, focus]\n",
+        "\n",
+        "bowl_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]"
+      ],
+      "metadata": {
+        "id": "dw5l3yOmB6lW"
+      },
+      "id": "dw5l3yOmB6lW",
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "9ea203f5-12bc-492a-a4ea-869fe46d1318",
+      "metadata": {
+        "id": "9ea203f5-12bc-492a-a4ea-869fe46d1318"
+      },
+      "outputs": [],
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE MATERIAL PROPERTIES\n",
+        "# =========================================================================\n",
+        "\n",
+        "# water\n",
+        "sound_speed = 1500.0 * np.ones(skull_mask.shape)\n",
+        "density = 1000.0 * np.ones(skull_mask.shape)\n",
+        "alpha_coeff = np.zeros(skull_mask.shape)\n",
+        "\n",
+        "# non-dispersive\n",
+        "alpha_power = 2.0\n",
+        "\n",
+        "# skull\n",
+        "sound_speed[skull_mask] = 2800.0\n",
+        "density[skull_mask] = 1850.0\n",
+        "alpha_coeff[skull_mask] = 4.0\n",
+        "\n",
+        "# brain\n",
+        "sound_speed[brain_mask] = 1560.0\n",
+        "density[brain_mask] = 1040.0\n",
+        "alpha_coeff[brain_mask] = 0.3\n",
+        "\n",
+        "c0_min = np.min(np.ravel(sound_speed))\n",
+        "c0_max = np.min(np.ravel(sound_speed))\n",
+        "\n",
+        "medium = kWaveMedium(sound_speed=sound_speed,\n",
+        "                     density=density,\n",
+        "                     alpha_coeff=alpha_coeff,\n",
+        "                     alpha_power=alpha_power)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE TRANSDUCER SETUP\n",
+        "# =========================================================================\n",
+        "\n",
+        "# bowl radius of curvature [m]\n",
+        "source_roc: float = 64.0e-3\n",
+        "\n",
+        "# as we will use the bowl element this has to be a int or float [m]\n",
+        "diameters: float = 64.0e-3\n",
+        "\n",
+        "# frequency [Hz]\n",
+        "freq: float = 500e3\n",
+        "\n",
+        "# source pressure [Pa]\n",
+        "source_amp = np.array([60e3])\n",
+        "\n",
+        "# phase [rad]\n",
+        "source_phase = np.array([0.0])"
+      ],
+      "metadata": {
+        "id": "xXwtEfzK23HI"
+      },
+      "id": "xXwtEfzK23HI",
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE COMPUTATIONAL PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "# wavelength [m]\n",
+        "k_min: float = c0_min / freq\n",
+        "\n",
+        "# points per wavelength\n",
+        "ppw: float = k_min / dx\n",
+        "\n",
+        "# number of periods to record\n",
+        "record_periods: int = 3\n",
+        "\n",
+        "# compute points per period\n",
+        "ppp: int = 30\n",
+        "\n",
+        "# CFL number determines time step\n",
+        "cfl: float = (ppw / ppp)"
+      ],
+      "metadata": {
+        "id": "FD2fqGiO27_e"
+      },
+      "id": "FD2fqGiO27_e",
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE KGRID\n",
+        "# =========================================================================\n",
+        "\n",
+        "grid_size_points = Vector([Nx, Ny, Nz])\n",
+        "\n",
+        "grid_spacing_meters = Vector([dx, dy, dz])\n",
+        "\n",
+        "# create the k-space grid\n",
+        "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+      ],
+      "metadata": {
+        "id": "PQb9R3Ub2_fE"
+      },
+      "id": "PQb9R3Ub2_fE",
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE TIME VECTOR\n",
+        "# =========================================================================\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+        "\n",
+        "dt_stability_limit = check_stability(kgrid, medium)\n",
+        "\n",
+        "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):\n",
+        "    dt_old = dt\n",
+        "    ppp = np.ceil(1.0 / (dt_stability_limit * freq))\n",
+        "    dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# calculate the number of time steps to reach steady state\n",
+        "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+        "\n",
+        "# create the time array using an integer number of points per period\n",
+        "Nt = round(t_end / dt)\n",
+        "\n",
+        "# make time array\n",
+        "kgrid.setTime(Nt, dt)\n",
+        "\n",
+        "# calculate the actual CFL after adjusting for dt\n",
+        "cfl_actual = 1.0 / (dt * freq)"
+      ],
+      "metadata": {
+        "id": "jo8NUNwY3F06"
+      },
+      "id": "jo8NUNwY3F06",
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SOURCE PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "# create empty kWaveArray this specfies the transducer properties\n",
+        "karray = kWaveArray(bli_tolerance=0.01,\n",
+        "                    upsampling_rate=16,\n",
+        "                    single_precision=True)\n",
+        "\n",
+        "# set bowl position and orientation\n",
+        "bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),\n",
+        "            kgrid.y_vec[bowl_coords[1]].item(),\n",
+        "            kgrid.z_vec[bowl_coords[2]].item()]\n",
+        "\n",
+        "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+        "             kgrid.y_vec[focus_coords[1]].item(),\n",
+        "             kgrid.z_vec[focus_coords[2]].item()]\n",
+        "\n",
+        "# add bowl shaped element\n",
+        "karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)\n",
+        "\n",
+        "# create time varying source\n",
+        "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+        "                               freq,\n",
+        "                               source_amp,\n",
+        "                               source_phase)\n",
+        "\n",
+        "# make a source object\n",
+        "source = kSource()\n",
+        "\n",
+        "# assign binary mask using the karray\n",
+        "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+        "\n",
+        "# assign source pressure output in time\n",
+        "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+      ],
+      "metadata": {
+        "id": "fYk2Rahs3GF7"
+      },
+      "id": "fYk2Rahs3GF7",
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SENSOR PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "sensor = kSensor()\n",
+        "\n",
+        "# set sensor mask: the mask says at which points data should be recorded\n",
+        "sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)\n",
+        "\n",
+        "# set the record type: record the pressure waveform\n",
+        "sensor.record = ['p']\n",
+        "\n",
+        "# record the final few periods when the field is in steady state\n",
+        "sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1"
+      ],
+      "metadata": {
+        "id": "JEqyvgsI3TF3"
+      },
+      "id": "JEqyvgsI3TF3",
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE SIMULATION PARAMETERS\n",
+        "# =========================================================================\n",
+        "\n",
+        "DATA_CAST = 'single'\n",
+        "DATA_PATH = '.'\n",
+        "\n",
+        "input_filename = tag + '_' + res + '_input.h5'\n",
+        "output_filename = tag + '_' + res + '_output.h5'\n",
+        "\n",
+        "# options for writing to file, but not doing simulations\n",
+        "simulation_options = SimulationOptions(\n",
+        "    data_cast=DATA_CAST,\n",
+        "    data_recast=True,\n",
+        "    save_to_disk=True,\n",
+        "    input_filename=input_filename,\n",
+        "    output_filename=output_filename,\n",
+        "    save_to_disk_exit=False,\n",
+        "    data_path=DATA_PATH,\n",
+        "    pml_inside=False)\n",
+        "\n",
+        "execution_options = SimulationExecutionOptions(\n",
+        "    is_gpu_simulation=True,\n",
+        "    delete_data=False,\n",
+        "    verbose_level=2)"
+      ],
+      "metadata": {
+        "id": "MYgWavhH3TNI"
+      },
+      "id": "MYgWavhH3TNI",
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# RUN THE SIMULATION\n",
+        "# =========================================================================\n",
+        "\n",
+        "sensor_data = kspaceFirstOrder3DG(\n",
+        "    medium=medium,\n",
+        "    kgrid=deepcopy(kgrid),\n",
+        "    source=source,\n",
+        "    sensor=sensor,\n",
+        "    simulation_options=simulation_options,\n",
+        "    execution_options=execution_options)"
+      ],
+      "metadata": {
+        "id": "02MyV_8e3JDQ"
+      },
+      "id": "02MyV_8e3JDQ",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# POST-PROCESS\n",
+        "# =========================================================================\n",
+        "\n",
+        "# sampling frequency\n",
+        "fs = 1.0 / kgrid.dt\n",
+        "\n",
+        "# get Fourier coefficients\n",
+        "amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1,\n",
+        "                              fft_padding=1, window='Rectangular')\n",
+        "\n",
+        "# reshape data: matlab uses Fortran ordering\n",
+        "p = np.reshape(amp, (Nx, Ny, Nz), order='F')\n",
+        "pmax = np.nanmax(p)\n",
+        "max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='C')\n",
+        "\n",
+        "p_water = np.empty_like(p)\n",
+        "p_water.fill(np.nan)\n",
+        "p_water[water_mask] = p[water_mask]\n",
+        "pmax_water = np.nanmax(p_water)\n",
+        "max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='C')\n",
+        "\n",
+        "p_skull = np.empty_like(p)\n",
+        "p_skull.fill(np.nan)\n",
+        "p_skull[skull_mask] = p[skull_mask]\n",
+        "pmax_skull = np.nanmax(p_skull)\n",
+        "max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='C')\n",
+        "\n",
+        "p_brain = np.empty_like(p)\n",
+        "p_brain.fill(np.nan)\n",
+        "p_brain[brain_mask] = p[brain_mask]\n",
+        "pmax_brain = np.nanmax(p_brain)\n",
+        "max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')\n",
+        "\n",
+        "# domain axes\n",
+        "x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)\n",
+        "y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)\n",
+        "z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)\n",
+        "\n",
+        "# colours\n",
+        "cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
+        "\n",
+        "# brain axes\n",
+        "# x\n",
+        "x_x_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]\n",
+        "y_x_brain = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]\n",
+        "coefficients_x_brain = np.polyfit(x_x_brain, y_x_brain, 1)\n",
+        "polynomial_x_brain = np.poly1d(coefficients_x_brain)\n",
+        "beam_axis_x_brain = polynomial_x_brain(z_vec)\n",
+        "\n",
+        "# y\n",
+        "x_y_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]\n",
+        "y_y_brain = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]\n",
+        "coefficients_y_brain = np.polyfit(x_y_brain, y_y_brain, 1)\n",
+        "polynomial_y_brain = np.poly1d(coefficients_y_brain)\n",
+        "beam_axis_y_brain = polynomial_y_brain(z_vec)\n",
+        "\n",
+        "# beam axis\n",
+        "beam_axis_brain = np.vstack((beam_axis_x_brain, beam_axis_y_brain, z_vec)).T\n",
+        "\n",
+        "# interpolate for pressure on brain axis\n",
+        "beam_pressure_brain = interpn((x_vec, y_vec, z_vec), p, beam_axis_brain,\n",
+        "                              method='linear', bounds_error=False, fill_value=np.nan)\n",
+        "\n",
+        "# skull axes\n",
+        "# x\n",
+        "x_x_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]\n",
+        "y_x_skull = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]\n",
+        "coefficients_x_skull = np.polyfit(x_x_skull, y_x_skull, 1)\n",
+        "polynomial_x_skull = np.poly1d(coefficients_x_skull)\n",
+        "beam_axis_x_skull = polynomial_x_skull(z_vec)\n",
+        "\n",
+        "# y\n",
+        "x_y_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]\n",
+        "y_y_skull = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]\n",
+        "coefficients_y_skull = np.polyfit(x_y_skull, y_y_skull, 1)\n",
+        "polynomial_y_skull = np.poly1d(coefficients_y_skull)\n",
+        "beam_axis_y_skull = polynomial_y_skull(z_vec)\n",
+        "\n",
+        "# beam axis\n",
+        "beam_axis_skull = np.vstack((beam_axis_x_skull, beam_axis_y_skull, z_vec)).T\n",
+        "\n",
+        "# interpolate for pressure\n",
+        "beam_pressure_skull = interpn((x_vec, y_vec, z_vec), p, beam_axis_skull,\n",
+        "                              method='linear', bounds_error=False,\n",
+        "                              fill_value=np.nan)\n",
+        "\n",
+        "# plot pressure on through centre lines\n",
+        "fig1, ax1 = plt.subplots()\n",
+        "ax1.plot(p[(Nx - 1) // 2, (Ny - 1) // 2, :] / 1e6, label='geometric')\n",
+        "ax1.plot(beam_pressure_brain / 1e6, label='focal')\n",
+        "ax1.plot(beam_pressure_skull / 1e6, label='skull')\n",
+        "ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)\n",
+        "ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)\n",
+        "ax1.set(xlabel='Axial Position [mm]',\n",
+        "        ylabel='Pressure [MPa]',\n",
+        "        title='Centreline Pressures')\n",
+        "ax1.legend()\n",
+        "ax1.grid(True)"
+      ],
+      "metadata": {
+        "id": "peGpEVT7y8EY",
+        "outputId": "71126d92-d068-44a1-ab27-914596c2de8d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "id": "peGpEVT7y8EY",
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_edges(mask, fill_with_nan=True):\n",
+        "    \"\"\"returns the mask as a float array and Np.NaN\"\"\"\n",
+        "    edges = find_boundaries(mask, mode='thin').astype(np.float32)\n",
+        "    if fill_with_nan:\n",
+        "        edges[edges == 0] = np.nan\n",
+        "    return edges\n",
+        "\n",
+        "# contouring block\n",
+        "edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "\n",
+        "contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)\n",
+        "contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)\n",
+        "contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Ny\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_x):\n",
+        "    j = np.argmax(np.where(contour_x[:, Nz // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_x[:, Nz // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "contours_x_inner = measure.find_contours(np.where(contour_x == i_inner, 1, 0))\n",
+        "if not contours_x_inner:\n",
+        "    print(\"size of contours_x_inner is zero\")\n",
+        "\n",
+        "contours_x_outer = measure.find_contours(np.where(contour_x == i_outer, 1, 0))\n",
+        "if not contours_x_outer:\n",
+        "   print(\"size of contours_x_outer is zero\")\n",
+        "\n",
+        "inner_index_x = float(Ny)\n",
+        "outer_index_x = float(0)\n",
+        "for i in range(len(contours_x_inner)):\n",
+        "    x_min = np.min(contours_x_inner[i][:, 1])\n",
+        "    if (x_min < inner_index_x):\n",
+        "        inner_index_x = i\n",
+        "for i in range(len(contours_x_outer)):\n",
+        "    x_max = np.max(contours_x_outer[i][:, 1])\n",
+        "    if (x_max > outer_index_x):\n",
+        "        outer_index_x = i\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Nx\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_y):\n",
+        "    j = np.argmax(np.where(contour_y[:, Nz // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_y[:, Nz // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "contours_y_inner = measure.find_contours(np.where(contour_y == i_inner, 1, 0))\n",
+        "if not contours_y_inner:\n",
+        "    print(\"size of contours_y_inner is zero\")\n",
+        "contours_y_outer = measure.find_contours(np.where(contour_y == i_outer, 1, 0))\n",
+        "if not contours_y_outer:\n",
+        "    print(\"size of contours_y_outer is zero\")\n",
+        "inner_index_y = float(Nx)\n",
+        "outer_index_y = float(0)\n",
+        "for i in range(len(contours_y_inner)):\n",
+        "    y_min = np.min(contours_y_inner[i][:, 1])\n",
+        "    if (y_min < inner_index_y):\n",
+        "        inner_index_y = i\n",
+        "for i in range(len(contours_y_outer)):\n",
+        "    y_max = np.max(contours_y_outer[i][:, 1])\n",
+        "    if (y_max > outer_index_y):\n",
+        "        outer_index_y = i\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Ny\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_z):\n",
+        "    j = np.argmax(np.where(contour_z[:, Nx // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_z[:, Nx // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "\n",
+        "contours_z_inner = measure.find_contours(np.where(contour_z == i_inner, 1, 0))\n",
+        "if not contours_z_inner:\n",
+        "    pass\n",
+        "else:\n",
+        "    inner_index_z = float(Nx)\n",
+        "    for i in range(len(contours_z_inner)):\n",
+        "        z_min = np.min(contours_z_inner[i][:, 1])\n",
+        "        if (z_min < inner_index_z):\n",
+        "            inner_index_z = i\n",
+        "\n",
+        "contours_z_outer = measure.find_contours(np.where(contour_z == i_outer, 1, 0))\n",
+        "if not contours_z_outer:\n",
+        "    pass\n",
+        "else:\n",
+        "    outer_index_z = float(0)\n",
+        "    for i in range(len(contours_z_outer)):\n",
+        "        z_max = np.max(contours_z_outer[i][:, 1])\n",
+        "        if (z_max > outer_index_z):\n",
+        "            outer_index_z = i\n",
+        "\n",
+        "# end of contouring block\n",
+        "edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))\n",
+        "edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))\n",
+        "edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int))\n",
+        "\n",
+        "# plot the pressure field at mid point along z axis\n",
+        "fig2, ax2 = plt.subplots()\n",
+        "im2 = ax2.pcolormesh(x_vec, y_vec, p[:, :, max_loc_brain[2]] / 1e6,\n",
+        "                 shading='gouraud',\n",
+        "                 cmap='viridis')\n",
+        "\n",
+        "if not contours_z_inner:\n",
+        "    ax2.pcolormesh(x_vec, y_vec, edges_z, shading='gouraud',\n",
+        "               cmap='Greys')\n",
+        "else:\n",
+        "    ax2.plot(contours_z_inner[inner_index_z][:, 1],\n",
+        "             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)\n",
+        "if not contours_z_outer:\n",
+        "    pass\n",
+        "else:\n",
+        "    ax2.plot(contours_z_outer[outer_index_z][:, 1],\n",
+        "             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)\n",
+        "#\n",
+        "ax2.set(xlabel=r'$x$ [mm]',\n",
+        "        ylabel=r'$y$ [mm]',\n",
+        "        title='Pressure Field')\n",
+        "ax2.grid(False)\n",
+        "divider2 = make_axes_locatable(ax2)\n",
+        "cax2 = divider2.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_2 = fig2.colorbar(im2, cax=cax2)\n",
+        "cbar_2.ax.set_title('[MPa]', fontsize='small')"
+      ],
+      "metadata": {
+        "id": "EaCNnASety_3",
+        "outputId": "288278ec-7900-4bc4-cbe2-9cacf5003b48",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        }
+      },
+      "id": "EaCNnASety_3",
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig3, (ax3a, ax3b) = plt.subplots(1, 2)\n",
+        "im3a = ax3a.pcolormesh(y_vec, z_vec, p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "\n",
+        "ax3a.plot(beam_axis_y_skull, z_vec, 'w--', lw=0.5)\n",
+        "ax3a.plot(beam_axis_y_brain, z_vec, 'w--', lw=0.5)\n",
+        "\n",
+        "# reversed and new underscore\n",
+        "ax3a.scatter(y_y_skull, x_y_skull, c='w', marker='o', edgecolors='none', alpha=0.5)\n",
+        "ax3a.scatter(y_y_brain, x_y_brain, c='w', marker='+', alpha=0.5)\n",
+        "\n",
+        "# ------------------------------------------------------------------------------\n",
+        "ax3a.plot(contours_x_inner[outer_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],\n",
+        "          contours_x_inner[outer_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "ax3a.plot(contours_x_outer[inner_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],\n",
+        "          contours_x_outer[inner_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "# ------------------------------------------------------------------------------\n",
+        "ax3a.grid(False)\n",
+        "ax3a.axes.get_yaxis().set_visible(False)\n",
+        "ax3a.axes.get_xaxis().set_visible(False)\n",
+        "divider3a = make_axes_locatable(ax3a)\n",
+        "cax3a = divider3a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3a = fig3.colorbar(im3a, cax=cax3a)\n",
+        "cbar_3a.ax.set_title('[MPa]', fontsize='small')\n",
+        "\n",
+        "ax3a.invert_yaxis()\n",
+        "\n",
+        "im3b = ax3b.pcolormesh(x_vec, z_vec, p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "\n",
+        "# ------------------------------------------------------------------------------\n",
+        "ax3b.plot(contours_y_inner[inner_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],\n",
+        "          contours_y_inner[inner_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "ax3b.plot(contours_y_outer[outer_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],\n",
+        "          contours_y_outer[outer_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "\n",
+        "ax3b.plot(beam_axis_x_skull, z_vec, 'w--', lw=0.5)\n",
+        "ax3b.plot(beam_axis_x_brain, z_vec, 'w--', lw=0.5)\n",
+        "ax3b.scatter(y_x_skull, x_x_skull, c='w', marker='o', edgecolors='none', alpha=0.5)\n",
+        "ax3b.scatter(y_x_brain, x_x_brain, c='w', marker='+', alpha=0.5)\n",
+        "\n",
+        "# ------------------------------------------------------------------------------\n",
+        "ax3b.grid(False)\n",
+        "ax3b.axes.get_yaxis().set_visible(False)\n",
+        "ax3b.axes.get_xaxis().set_visible(False)\n",
+        "divider3b = make_axes_locatable(ax3b)\n",
+        "cax3b = divider3b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3b = fig3.colorbar(im3b, cax=cax3b)\n",
+        "cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})\n",
+        "ax3b.invert_yaxis()"
+      ],
+      "metadata": {
+        "id": "u0c6bDpruEJb",
+        "outputId": "815c875e-f4b8-4862-a8fe-f89eec44376b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        }
+      },
+      "id": "u0c6bDpruEJb",
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig5, (ax5a, ax5b) = plt.subplots(1, 2)\n",
+        "im5a = ax5a.imshow(p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                   vmin=0, vmax=pmax / 1e6,\n",
+        "                   aspect='auto',\n",
+        "                   interpolation='none',\n",
+        "                   origin='upper',\n",
+        "                   cmap='viridis')\n",
+        "im5a_boundary = ax5a.imshow(edges_x, aspect='auto', interpolation='none',\n",
+        "                            cmap='Greys', origin='upper', alpha=0.75)\n",
+        "ax5a.grid(False)\n",
+        "ax5a.axes.get_yaxis().set_visible(False)\n",
+        "ax5a.axes.get_xaxis().set_visible(False)\n",
+        "divider5a = make_axes_locatable(ax5a)\n",
+        "cax5a = divider5a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_5a = fig5.colorbar(im5a, cax=cax5a)\n",
+        "cbar_5a.ax.set_title('[MPa]', fontsize='small')\n",
+        "im5b = ax5b.imshow(p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                   vmin=0, vmax=pmax / 1e6,\n",
+        "                   aspect='auto',\n",
+        "                   interpolation='none',\n",
+        "                   origin='upper',\n",
+        "                   cmap='viridis')\n",
+        "im5b_boundary = ax5b.imshow(edges_y, aspect='auto', interpolation='none',\n",
+        "                            cmap='Greys', origin='upper', alpha=0.75)\n",
+        "ax5b.grid(False)\n",
+        "ax5b.axes.get_yaxis().set_visible(False)\n",
+        "ax5b.axes.get_xaxis().set_visible(False)\n",
+        "divider5b = make_axes_locatable(ax5b)\n",
+        "cax5b = divider5b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_5b = fig5.colorbar(im5b, cax=cax5b)\n",
+        "_= cbar_5b.ax.set_title('[MPa]', fontsize='small')"
+      ],
+      "metadata": {
+        "id": "xg4yXqWevHYq",
+        "outputId": "612052b9-bd54-45ec-b371-735af5996ba1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 433
+        }
+      },
+      "id": "xg4yXqWevHYq",
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "all_contours_x = []\n",
+        "for i in range(num_x):\n",
+        "    all_contours_x.append(measure.find_contours(np.where(contour_x == (i + 1), 1, 0)))\n",
+        "\n",
+        "all_contours_y = []\n",
+        "for i in range(num_y):\n",
+        "    all_contours_y.append(measure.find_contours(np.where(contour_y == (i + 1), 1, 0)))\n",
+        "\n",
+        "fig6, (ax6a, ax6b) = plt.subplots(1, 2)\n",
+        "im6a = ax6a.imshow(p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                   vmin=0, vmax=pmax / 1e6,\n",
+        "                   aspect='auto',\n",
+        "                   interpolation='none',\n",
+        "                   origin='upper',\n",
+        "                   cmap='viridis')\n",
+        "for contour in all_contours_x:\n",
+        "    for i in range(len(contour)):\n",
+        "        ax6a.plot(contour[i][:, 1], contour[i][:, 0], 'w', linewidth=0.5)\n",
+        "ax6a.grid(False)\n",
+        "ax6a.axes.get_yaxis().set_visible(False)\n",
+        "ax6a.axes.get_xaxis().set_visible(False)\n",
+        "divider6a = make_axes_locatable(ax5a)\n",
+        "cax6a = divider6a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_6a = fig6.colorbar(im6a, cax=cax6a)\n",
+        "cbar_6a.ax.set_title('[MPa]', fontsize='small')\n",
+        "im6b = ax6b.imshow(p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                   vmin=0, vmax=pmax / 1e6,\n",
+        "                   aspect='auto',\n",
+        "                   interpolation='none',\n",
+        "                   origin='upper',\n",
+        "                   cmap='viridis')\n",
+        "\n",
+        "custom_cycler = cycler(ls=['-', '--', ':', '-.'])\n",
+        "\n",
+        "ax6b.set_prop_cycle(custom_cycler)\n",
+        "\n",
+        "for idx, contour in enumerate(all_contours_y):\n",
+        "    for i in range(len(contour)):\n",
+        "        ax6b.plot(contour[i][:, 1], contour[i][:, 0], c=cycle[idx],\n",
+        "                  linewidth=0.5, label=str(idx))\n",
+        "ax6b.legend()\n",
+        "ax6b.grid(False)\n",
+        "ax6b.axes.get_yaxis().set_visible(False)\n",
+        "ax6b.axes.get_xaxis().set_visible(False)\n",
+        "divider6b = make_axes_locatable(ax6b)\n",
+        "cax6b = divider6b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_6b = fig6.colorbar(im6b, cax=cax6b)\n",
+        "cbar_6b.ax.set_title('[MPa]', fontsize='small')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "zCGVmC8JvIbQ",
+        "outputId": "264881c7-64e3-454a-d932-8e6283c9373e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 433
+        }
+      },
+      "id": "zCGVmC8JvIbQ",
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/benchmarks/7/ph1-bm7-sc1.py b/examples/benchmarks/7/ph1-bm7-sc1.py
new file mode 100644
index 000000000..7ef242176
--- /dev/null
+++ b/examples/benchmarks/7/ph1-bm7-sc1.py
@@ -0,0 +1,723 @@
+import numpy as np
+
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+from matplotlib.colors import Normalize
+import matplotlib.cm as cm
+
+from cycler import cycler
+
+import h5py
+
+from skimage import measure
+from skimage.segmentation import find_boundaries
+from scipy.interpolate import interpn
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+verbose: bool = True
+savePlotting: bool = False
+useMaxTimeStep: bool = True
+
+tag = 'bm7'
+res = '1mm'
+
+mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'
+
+data = h5py.File(mask_filename, 'r')
+
+# is given in millimetres
+dx = data['dx'][:].item()
+
+# scale to metres
+dx = dx / 1000.0
+dy = dx
+dz = dx
+
+xi = np.squeeze(np.asarray(data['xi'][:]))
+yi = np.squeeze(np.asarray(data['yi'][:]))
+zi = np.squeeze(np.asarray(data['zi'][:]))
+
+matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]
+
+skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)
+brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)
+
+skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')
+brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')
+
+water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))
+water_mask = water_mask.astype(bool)
+
+skull_mask = np.swapaxes(skull_mask, 0, 2)
+brain_mask = np.swapaxes(brain_mask, 0, 2)
+water_mask = np.swapaxes(water_mask, 0, 2)
+
+Nx, Ny, Nz = skull_mask.shape
+
+focus = int(64 / data['dx'][:].item())
+
+focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, focus]
+
+bowl_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones(skull_mask.shape)
+density = 1000.0 * np.ones(skull_mask.shape)
+alpha_coeff = np.zeros(skull_mask.shape)
+
+# non-dispersive
+alpha_power = 2.0
+
+# skull
+sound_speed[skull_mask] = 2800.0
+density[skull_mask] = 1850.0
+alpha_coeff[skull_mask] = 4.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(sound_speed.flatten())
+c0_max = np.min(sound_speed.flatten())
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# bowl radius of curvature [m]
+source_roc: float = 64.0e-3
+
+# as we will use the bowl element this has to be a int or float
+diameters: float = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 30
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Ny, Nz])
+
+grid_spacing_meters = Vector([dx, dy, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item(),
+            kgrid.z_vec[bowl_coords[2]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item(),
+             kgrid.z_vec[focus_coords[2]].item()]
+
+# add bowl shaped element
+karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - (record_periods * ppp) + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = 'data/'
+
+input_filename = tag + '_' + res + '_input.h5'
+output_filename = tag + '_' + res + '_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspaceFirstOrder3DG(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# POST-PROCESS
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')
+
+# reshape data: matlab uses Fortran ordering
+p = np.reshape(amp, (Nx, Ny, Nz), order='F')
+pmax = np.nanmax(p)
+max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='C')
+
+p_water = np.empty_like(p)
+p_water.fill(np.nan)
+p_water[water_mask] = p[water_mask]
+pmax_water = np.nanmax(p_water)
+max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='C')
+
+p_skull = np.empty_like(p)
+p_skull.fill(np.nan)
+p_skull[skull_mask] = p[skull_mask]
+pmax_skull = np.nanmax(p_skull)
+max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='C')
+
+p_brain = np.empty_like(p)
+p_brain.fill(np.nan)
+p_brain[brain_mask] = p[brain_mask]
+pmax_brain = np.nanmax(p_brain)
+max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')
+
+# domain axes
+x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)
+y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)
+z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+
+# colours
+cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
+
+# brain axes
+# x
+x_x_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_x_brain = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]
+coefficients_x_brain = np.polyfit(x_x_brain, y_x_brain, 1)
+polynomial_x_brain = np.poly1d(coefficients_x_brain)
+beam_axis_x_brain = polynomial_x_brain(z_vec)
+# y
+x_y_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_y_brain = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]
+coefficients_y_brain = np.polyfit(x_y_brain, y_y_brain, 1)
+polynomial_y_brain = np.poly1d(coefficients_y_brain)
+beam_axis_y_brain = polynomial_y_brain(z_vec)
+# beam axis
+beam_axis_brain = np.vstack((beam_axis_x_brain, beam_axis_y_brain, z_vec)).T
+# interpolate for pressure on brain axis
+beam_pressure_brain = interpn((x_vec, y_vec, z_vec), p, beam_axis_brain,
+                              method='linear', bounds_error=False, fill_value=np.nan)
+
+# skull axes
+# x
+x_x_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_x_skull = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]
+coefficients_x_skull = np.polyfit(x_x_skull, y_x_skull, 1)
+polynomial_x_skull = np.poly1d(coefficients_x_skull)
+beam_axis_x_skull = polynomial_x_skull(z_vec)
+# y
+x_y_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_y_skull = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]
+coefficients_y_skull = np.polyfit(x_y_skull, y_y_skull, 1)
+polynomial_y_skull = np.poly1d(coefficients_y_skull)
+beam_axis_y_skull = polynomial_y_skull(z_vec)
+# beam axis
+beam_axis_skull = np.vstack((beam_axis_x_skull, beam_axis_y_skull, z_vec)).T
+# interpolate for pressure
+beam_pressure_skull = interpn((x_vec, y_vec, z_vec), p, beam_axis_skull,
+                              method='linear', bounds_error=False,
+                              fill_value=np.nan)
+
+# plot pressure on through centre lines
+fig1, ax1 = plt.subplots()
+ax1.plot(p[(Nx - 1) // 2, (Ny - 1) // 2, :] / 1e6, label='geometric')
+ax1.plot(beam_pressure_brain / 1e6, label='focal')
+ax1.plot(beam_pressure_skull / 1e6, label='skull')
+ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)
+ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)
+ax1.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [MPa]',
+        title='Centreline Pressures')
+ax1.legend()
+ax1.grid(True)
+
+
+
+def get_edges(mask, fill_with_nan=True):
+    """returns the mask as a float array and Np.NaN"""
+    edges = find_boundaries(mask, mode='thin').astype(np.float32)
+    if fill_with_nan:
+        edges[edges == 0] = np.nan
+    return edges
+
+
+# contouring block
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int),
+                    fill_with_nan=False)
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int),
+                    fill_with_nan=False)
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int),
+                    fill_with_nan=False)
+
+contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)
+contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)
+contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_x):
+    j = np.argmax(np.where(contour_x[:, Nz // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_x[:, Nz // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+contours_x_inner = measure.find_contours(np.where(contour_x == i_inner, 1, 0))
+if not contours_x_inner:
+    pass
+contours_x_outer = measure.find_contours(np.where(contour_x == i_outer, 1, 0))
+if not contours_x_outer:
+   pass
+inner_index_x = float(Ny)
+outer_index_x = float(0)
+for i in range(len(contours_x_inner)):
+    x_min = np.min(contours_x_inner[i][:, 1])
+    if (x_min < inner_index_x):
+        inner_index_x = i
+for i in range(len(contours_x_outer)):
+    x_max = np.max(contours_x_outer[i][:, 1])
+    if (x_max > outer_index_x):
+        outer_index_x = i
+
+jmax = 0
+jmin = Nx
+i_inner = None
+i_outer = None
+for i in range(num_y):
+    j = np.argmax(np.where(contour_y[:, Nz // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_y[:, Nz // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+contours_y_inner = measure.find_contours(np.where(contour_y == i_inner, 1, 0))
+if not contours_y_inner:
+    pass
+contours_y_outer = measure.find_contours(np.where(contour_y == i_outer, 1, 0))
+if not contours_y_outer:
+    pass
+inner_index_y = float(Nx)
+outer_index_y = float(0)
+for i in range(len(contours_y_inner)):
+    y_min = np.min(contours_y_inner[i][:, 1])
+    if (y_min < inner_index_y):
+        inner_index_y = i
+for i in range(len(contours_y_outer)):
+    y_max = np.max(contours_y_outer[i][:, 1])
+    if (y_max > outer_index_y):
+        outer_index_y = i
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_z):
+    j = np.argmax(np.where(contour_z[:, Nx // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_z[:, Nx // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+
+contours_z_inner = measure.find_contours(np.where(contour_z == i_inner, 1, 0))
+if not contours_z_inner:
+    pass
+else:
+    inner_index_z = float(Nx)
+    for i in range(len(contours_z_inner)):
+        z_min = np.min(contours_z_inner[i][:, 1])
+        if (z_min < inner_index_z):
+            inner_index_z = i
+
+contours_z_outer = measure.find_contours(np.where(contour_z == i_outer, 1, 0))
+if not contours_z_outer:
+    pass
+else:
+    outer_index_z = float(0)
+    for i in range(len(contours_z_outer)):
+        z_max = np.max(contours_z_outer[i][:, 1])
+        if (z_max > outer_index_z):
+            outer_index_z = i
+
+# end of contouring block
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int))
+
+# plot the pressure field at mid point along z axis
+fig2, ax2 = plt.subplots()
+im2 = ax2.imshow(p[:, :, max_loc_brain[2]] / 1e6,
+                 aspect='auto',
+                 interpolation='none',
+                 origin='lower',
+                 cmap='viridis')
+
+if not contours_z_inner:
+    ax2.imshow(edges_z, aspect='auto', interpolation='none',
+               cmap='Greys', origin='upper')
+else:
+    ax2.plot(contours_z_inner[inner_index_z][:, 1],
+             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax2.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+#
+ax2.set(xlabel=r'$x$ [mm]',
+        ylabel=r'$y$ [mm]',
+        title='Pressure Field')
+ax2.grid(False)
+divider2 = make_axes_locatable(ax2)
+cax2 = divider2.append_axes("right", size="5%", pad=0.05)
+cbar_2 = fig2.colorbar(im2, cax=cax2)
+cbar_2.ax.set_title('[MPa]', fontsize='small')
+
+pwater_max_x = np.nanmax(p_water[max_loc_brain[0], :, :].flatten())
+pskull_max_x = np.nanmax(p_skull[max_loc_brain[0], :, :].flatten())
+pbrain_max_x = np.nanmax(p_brain[max_loc_brain[0], :, :].flatten())
+
+pwater_max_y = np.nanmax(p_water[:, max_loc_brain[1], :].flatten())
+pskull_max_y = np.nanmax(p_skull[:, max_loc_brain[1], :].flatten())
+pbrain_max_y = np.nanmax(p_brain[:, max_loc_brain[1], :].flatten())
+
+px_vec = np.linspace(kgrid.x_vec[0].item() - kgrid.dx,
+                     kgrid.x_vec[-1].item() + kgrid.dx, kgrid.Nx + 1)
+py_vec = np.linspace(kgrid.y_vec[0].item() - kgrid.dy,
+                     kgrid.y_vec[-1].item() + kgrid.dy, kgrid.Ny + 1)
+pz_vec = np.linspace(kgrid.z_vec[0].item() - kgrid.dz,
+                     kgrid.z_vec[-1].item() + kgrid.dz, kgrid.Nz + 1)
+
+fig3, (ax3a, ax3b) = plt.subplots(1, 2)
+im3a = ax3a.pcolormesh(y_vec, z_vec, p[max_loc_brain[0], :, :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+
+ax3a.plot(beam_axis_y_skull, z_vec, 'w--', lw=0.5)
+ax3a.plot(beam_axis_y_brain, z_vec, 'w--', lw=0.5)
+
+# reversed and new underscore
+ax3a.scatter(y_y_skull, x_y_skull, c='w', marker='o', edgecolors='none', alpha=0.5)
+ax3a.scatter(y_y_brain, x_y_brain, c='w', marker='+', alpha=0.5)
+
+# ------------------------------------------------------------------------------
+ax3a.plot(contours_x_inner[outer_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],
+          contours_x_inner[outer_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+ax3a.plot(contours_x_outer[inner_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],
+          contours_x_outer[inner_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+# ------------------------------------------------------------------------------
+ax3a.grid(False)
+ax3a.axes.get_yaxis().set_visible(False)
+ax3a.axes.get_xaxis().set_visible(False)
+divider3a = make_axes_locatable(ax3a)
+cax3a = divider3a.append_axes("right", size="5%", pad=0.05)
+cbar_3a = fig3.colorbar(im3a, cax=cax3a)
+cbar_3a.ax.set_title('[MPa]', fontsize='small')
+
+ax3a.invert_yaxis()
+
+im3b = ax3b.pcolormesh(x_vec, z_vec, p[:, max_loc_brain[1], :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+
+# ------------------------------------------------------------------------------
+ax3b.plot(contours_y_inner[inner_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],
+          contours_y_inner[inner_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+ax3b.plot(contours_y_outer[outer_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],
+          contours_y_outer[outer_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+
+ax3b.plot(beam_axis_x_skull, z_vec, 'w--', lw=0.5)
+ax3b.plot(beam_axis_x_brain, z_vec, 'w--', lw=0.5)
+ax3b.scatter(y_x_skull, x_x_skull, c='w', marker='o', edgecolors='none', alpha=0.5)
+ax3b.scatter(y_x_brain, x_x_brain, c='w', marker='+', alpha=0.5)
+
+# ------------------------------------------------------------------------------
+ax3b.grid(False)
+ax3b.axes.get_yaxis().set_visible(False)
+ax3b.axes.get_xaxis().set_visible(False)
+divider3b = make_axes_locatable(ax3b)
+cax3b = divider3b.append_axes("right", size="5%", pad=0.05)
+cbar_3b = fig3.colorbar(im3b, cax=cax3b)
+cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})
+ax3b.invert_yaxis()
+
+fig4, ax4 = plt.subplots(1, 2)
+(ax4a, ax4b) = ax4
+cmap = cm.viridis
+normalizer = Normalize(vmin=0, vmax=pmax / 1e6, clip=False)
+im = cm.ScalarMappable(norm=normalizer, cmap='viridis')
+
+p_source_index = np.squeeze(
+    np.array(h5py.File(DATA_PATH + input_filename, mode='r')["p_source_index"]))
+
+pml_x_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_x_size"]))
+pml_x_size = pml_x_size[()]
+pml_y_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_y_size"]))
+pml_y_size = pml_y_size[()]
+pml_z_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_z_size"]))
+pml_z_size = pml_z_size[()]
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Nx + pml_x_size, source.p_mask.shape[0], dtype=int), 0)
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Ny + pml_y_size, source.p_mask.shape[1], dtype=int), 1)
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Nz + pml_z_size, source.p_mask.shape[2], dtype=int), 2)
+
+source.p_mask.astype(float)
+
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_x_size, dtype=int), 0)
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_y_size, dtype=int), 1)
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_z_size, dtype=int), 2)
+
+tx = np.empty_like(p)
+tx.fill(np.nan)
+tx.flatten()[p_source_index] = 1
+tx.reshape(p.shape, order='F')
+
+im4a = ax4a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap=cmap)
+
+im4a_boundary = ax4a.imshow(edges_x, aspect='auto',
+                            interpolation='none', origin='upper', cmap='Greys')
+
+fig5, (ax5a, ax5b) = plt.subplots(1, 2)
+im5a = ax5a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5a_boundary = ax5a.imshow(edges_x, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5a.grid(False)
+ax5a.axes.get_yaxis().set_visible(False)
+ax5a.axes.get_xaxis().set_visible(False)
+divider5a = make_axes_locatable(ax5a)
+cax5a = divider5a.append_axes("right", size="5%", pad=0.05)
+cbar_5a = fig5.colorbar(im5a, cax=cax5a)
+cbar_5a.ax.set_title('[MPa]', fontsize='small')
+im5b = ax5b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5b_boundary = ax5b.imshow(edges_y, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5b.grid(False)
+ax5b.axes.get_yaxis().set_visible(False)
+ax5b.axes.get_xaxis().set_visible(False)
+divider5b = make_axes_locatable(ax5b)
+cax5b = divider5b.append_axes("right", size="5%", pad=0.05)
+cbar_5b = fig5.colorbar(im5b, cax=cax5b)
+cbar_5b.ax.set_title('[MPa]', fontsize='small')
+
+all_contours_x = []
+for i in range(num_x):
+    all_contours_x.append(measure.find_contours(np.where(contour_x == (i + 1), 1, 0)))
+
+all_contours_y = []
+for i in range(num_y):
+    all_contours_y.append(measure.find_contours(np.where(contour_y == (i + 1), 1, 0)))
+
+fig6, (ax6a, ax6b) = plt.subplots(1, 2)
+im6a = ax6a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+for contour in all_contours_x:
+    for i in range(len(contour)):
+        ax6a.plot(contour[i][:, 1], contour[i][:, 0], 'w', linewidth=0.5)
+ax6a.grid(False)
+ax6a.axes.get_yaxis().set_visible(False)
+ax6a.axes.get_xaxis().set_visible(False)
+divider6a = make_axes_locatable(ax5a)
+cax6a = divider6a.append_axes("right", size="5%", pad=0.05)
+cbar_6a = fig6.colorbar(im6a, cax=cax6a)
+cbar_6a.ax.set_title('[MPa]', fontsize='small')
+im6b = ax6b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+
+custom_cycler = cycler(ls=['-', '--', ':', '-.'])
+
+ax6b.set_prop_cycle(custom_cycler)
+
+for idx, contour in enumerate(all_contours_y):
+    for i in range(len(contour)):
+        ax6b.plot(contour[i][:, 1], contour[i][:, 0], c=cycle[idx],
+                  linewidth=0.5, label=str(idx))
+ax6b.legend()
+ax6b.grid(False)
+ax6b.axes.get_yaxis().set_visible(False)
+ax6b.axes.get_xaxis().set_visible(False)
+divider6b = make_axes_locatable(ax6b)
+cax6b = divider6b.append_axes("right", size="5%", pad=0.05)
+cbar_6b = fig6.colorbar(im6b, cax=cax6b)
+cbar_6b.ax.set_title('[MPa]', fontsize='small')
+
+plt.show()
diff --git a/examples/benchmarks/7/ph1-bm7-sc2.ipynb b/examples/benchmarks/7/ph1-bm7-sc2.ipynb
new file mode 100644
index 000000000..9a53562e5
--- /dev/null
+++ b/examples/benchmarks/7/ph1-bm7-sc2.ipynb
@@ -0,0 +1,1027 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyNquu40Gth6uO4K/xeEyW/m",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/djps/k-wave-python/blob/benchmarks/examples/benchmarks/7/ph1-bm7-sc2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "MIx0Wj7lhBRi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b04a62f9-c8a5-4242-dc9a-833b0e1b1736"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/waltsims/k-wave-python\n",
+            "  Cloning https://github.com/waltsims/k-wave-python to /tmp/pip-req-build-mhwxqyus\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/waltsims/k-wave-python /tmp/pip-req-build-mhwxqyus\n",
+            "  Resolved https://github.com/waltsims/k-wave-python to commit d8f56f34eea9d1e68f72bce3708a9fc55f2fb71f\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: beartype==0.16.4 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (0.16.4)\n",
+            "Requirement already satisfied: deepdiff==6.7.1 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (6.7.1)\n",
+            "Requirement already satisfied: h5py==3.10.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.10.0)\n",
+            "Requirement already satisfied: matplotlib==3.8.3 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.8.3)\n",
+            "Requirement already satisfied: nptyping==2.5.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (2.5.0)\n",
+            "Requirement already satisfied: numpy<1.27.0,>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.25.2)\n",
+            "Requirement already satisfied: opencv-python==4.9.0.80 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (4.9.0.80)\n",
+            "Requirement already satisfied: scipy==1.12.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.12.0)\n",
+            "Requirement already satisfied: ordered-set<4.2.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from deepdiff==6.7.1->k-Wave-python==0.3.2) (4.1.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (4.49.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (23.2)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (2.8.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib==3.8.3->k-Wave-python==0.3.2) (1.16.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/waltsims/k-wave-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from copy import deepcopy\n",
+        "import requests\n",
+        "import shutil\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "\n",
+        "from matplotlib.colors import Normalize\n",
+        "import matplotlib.cm as cm\n",
+        "\n",
+        "from cycler import cycler\n",
+        "\n",
+        "import h5py\n",
+        "\n",
+        "from skimage import measure\n",
+        "from skimage.segmentation import find_boundaries\n",
+        "from scipy.interpolate import interpn\n",
+        "\n",
+        "from kwave.data import Vector\n",
+        "from kwave.utils.kwave_array import kWaveArray\n",
+        "from kwave.utils.checks import check_stability\n",
+        "from kwave.kgrid import kWaveGrid\n",
+        "from kwave.kmedium import kWaveMedium\n",
+        "from kwave.ksource import kSource\n",
+        "from kwave.ksensor import kSensor\n",
+        "from kwave.utils.signals import create_cw_signals\n",
+        "from kwave.utils.filters import extract_amp_phase\n",
+        "from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG\n",
+        "\n",
+        "from kwave.options.simulation_options import SimulationOptions\n",
+        "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+        "\n",
+        "verbose: bool = True\n",
+        "savePlotting: bool = False\n",
+        "useMaxTimeStep: bool = True"
+      ],
+      "metadata": {
+        "id": "eLqMa3-xhGwg"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load data"
+      ],
+      "metadata": {
+        "id": "iiB2dv3wV2bd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tag = 'bm7'\n",
+        "res = '1mm'\n",
+        "\n",
+        "mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'\n",
+        "\n",
+        "url = 'https://raw.githubusercontent.com/djps/k-wave-python/benchmarks/examples/benchmarks/7/skull_mask_bm7_dx_1mm.mat'\n",
+        "mask_filename = requests.get(url, stream=True)\n",
+        "mask_filename.raw.decode_content = True\n",
+        "with open(\"temp.h5\", \"wb\") as _fh:\n",
+        "    shutil.copyfileobj(mask_filename.raw, _fh)\n",
+        "data = h5py.File(\"temp.h5\", mode='r')\n",
+        "\n",
+        "# is given in millimetres\n",
+        "dx = data['dx'][:].item()\n",
+        "\n",
+        "# scale to metres\n",
+        "dx = dx / 1000.0\n",
+        "dy = dx\n",
+        "dz = dx\n",
+        "\n",
+        "xi = np.squeeze(np.asarray(data['xi'][:]))\n",
+        "yi = np.squeeze(np.asarray(data['yi'][:]))\n",
+        "zi = np.squeeze(np.asarray(data['zi'][:]))\n",
+        "\n",
+        "matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]\n",
+        "\n",
+        "skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)\n",
+        "brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)\n",
+        "\n",
+        "skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')\n",
+        "brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')\n",
+        "\n",
+        "water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))\n",
+        "water_mask = water_mask.astype(bool)\n",
+        "\n",
+        "skull_mask = np.swapaxes(skull_mask, 0, 2)\n",
+        "brain_mask = np.swapaxes(brain_mask, 0, 2)\n",
+        "water_mask = np.swapaxes(water_mask, 0, 2)\n",
+        "\n",
+        "Nx, Ny, Nz = skull_mask.shape\n",
+        "\n",
+        "focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, (Nz - 1) // 2]\n",
+        "\n",
+        "disc_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]"
+      ],
+      "metadata": {
+        "id": "ib-BCt4UV0Y1"
+      },
+      "execution_count": 51,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Assign medium properties"
+      ],
+      "metadata": {
+        "id": "gSFpPRzMV4gs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# water\n",
+        "sound_speed = 1500.0 * np.ones(skull_mask.shape)\n",
+        "density = 1000.0 * np.ones(skull_mask.shape)\n",
+        "alpha_coeff = np.zeros(skull_mask.shape)\n",
+        "\n",
+        "# non-dispersive\n",
+        "alpha_power = 2.0\n",
+        "\n",
+        "# skull\n",
+        "sound_speed[skull_mask] = 2800.0\n",
+        "density[skull_mask] = 1850.0\n",
+        "alpha_coeff[skull_mask] = 4.0\n",
+        "\n",
+        "# brain\n",
+        "sound_speed[brain_mask] = 1560.0\n",
+        "density[brain_mask] = 1040.0\n",
+        "alpha_coeff[brain_mask] = 0.3\n",
+        "\n",
+        "c0_min = np.min(sound_speed.flatten())\n",
+        "c0_max = np.min(sound_speed.flatten())\n",
+        "\n",
+        "medium = kWaveMedium(sound_speed=sound_speed,\n",
+        "                     density=density,\n",
+        "                     alpha_coeff=alpha_coeff,\n",
+        "                     alpha_power=alpha_power)"
+      ],
+      "metadata": {
+        "id": "Ojvy5GP7hRfZ"
+      },
+      "execution_count": 52,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the transducer\n"
+      ],
+      "metadata": {
+        "id": "8eXxWV1BV7ZE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# diameter of the disc\n",
+        "diameter = 20e-3\n",
+        "\n",
+        "# frequency [Hz]\n",
+        "freq = 500e3\n",
+        "\n",
+        "# source pressure [Pa]\n",
+        "source_amp = np.array([60e3])\n",
+        "\n",
+        "# phase [rad]\n",
+        "source_phase = np.array([0.0])"
+      ],
+      "metadata": {
+        "id": "e1TvagiThYjy"
+      },
+      "execution_count": 53,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define simulation parameters"
+      ],
+      "metadata": {
+        "id": "cEtCMXKwWES2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# wavelength\n",
+        "k_min: float = c0_min / freq\n",
+        "\n",
+        "# points per wavelength\n",
+        "ppw: float = k_min / dx\n",
+        "\n",
+        "# number of periods to record\n",
+        "record_periods: int = 3\n",
+        "\n",
+        "# compute points per period\n",
+        "ppp: int = 30\n",
+        "\n",
+        "# CFL number determines time step\n",
+        "cfl: float = (ppw / ppp)"
+      ],
+      "metadata": {
+        "id": "WoxSptBfhZhG"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Setup the grid"
+      ],
+      "metadata": {
+        "id": "BOIrzTo_WI0_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "grid_size_points = Vector([Nx, Ny, Nz])\n",
+        "\n",
+        "grid_spacing_meters = Vector([dx, dy, dz])\n",
+        "\n",
+        "# create the k-space grid\n",
+        "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+      ],
+      "metadata": {
+        "id": "2x2MU1A7h_Pw"
+      },
+      "execution_count": 55,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define time-vector"
+      ],
+      "metadata": {
+        "id": "LnlxMMbwWLdg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# compute corresponding time stepping\n",
+        "dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+        "\n",
+        "dt_stability_limit = check_stability(kgrid, medium)\n",
+        "\n",
+        "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):\n",
+        "    dt_old = dt\n",
+        "    ppp = np.ceil(1.0 / (dt_stability_limit * freq))\n",
+        "    dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# calculate the number of time steps to reach steady state\n",
+        "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+        "\n",
+        "# create the time array using an integer number of points per period\n",
+        "Nt = round(t_end / dt)\n",
+        "\n",
+        "# make time array\n",
+        "kgrid.setTime(Nt, dt)\n",
+        "\n",
+        "# calculate the actual CFL after adjusting for dt\n",
+        "cfl_actual = 1.0 / (dt * freq)"
+      ],
+      "metadata": {
+        "id": "iE_dy15yiBgO"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Setup the source parameters"
+      ],
+      "metadata": {
+        "id": "Nj7rwb24WO1K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# create empty kWaveArray this specfies the transducer properties\n",
+        "karray = kWaveArray(bli_tolerance=0.01,\n",
+        "                    upsampling_rate=16,\n",
+        "                    single_precision=True)\n",
+        "\n",
+        "# set disc position\n",
+        "position = [kgrid.x_vec[disc_coords[0]].item(),\n",
+        "            kgrid.y_vec[disc_coords[1]].item(),\n",
+        "            kgrid.z_vec[disc_coords[2]].item()]\n",
+        "\n",
+        "# arbitrary position\n",
+        "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+        "             kgrid.y_vec[focus_coords[1]].item(),\n",
+        "             kgrid.z_vec[focus_coords[2]].item()]\n",
+        "\n",
+        "# add disc-shaped planar element\n",
+        "karray.add_disc_element(position, diameter, focus_pos)\n",
+        "\n",
+        "# create time varying source\n",
+        "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+        "                               freq,\n",
+        "                               source_amp,\n",
+        "                               source_phase)\n",
+        "\n",
+        "# make a source object.\n",
+        "source = kSource()\n",
+        "\n",
+        "# assign binary mask using the karray\n",
+        "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+        "\n",
+        "# assign source pressure output in time\n",
+        "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+      ],
+      "metadata": {
+        "id": "a8u1eSKKiEPS"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Set the sensors"
+      ],
+      "metadata": {
+        "id": "7m5pMOXLWVPz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sensor = kSensor()\n",
+        "\n",
+        "# set sensor mask: the mask says at which points data should be recorded\n",
+        "sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)\n",
+        "\n",
+        "# set the record type: record the pressure waveform\n",
+        "sensor.record = ['p']\n",
+        "\n",
+        "# record the final few periods when the field is in steady state\n",
+        "sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1"
+      ],
+      "metadata": {
+        "id": "PC40TsIoiHN2"
+      },
+      "execution_count": 58,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Setup and run the simulation"
+      ],
+      "metadata": {
+        "id": "zh65srhDWYpR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "DATA_CAST = 'single'\n",
+        "DATA_PATH = './'\n",
+        "\n",
+        "input_filename = tag + '_' + res + '_input.h5'\n",
+        "output_filename = tag + '_' + res + '_output.h5'\n",
+        "\n",
+        "# options for writing to file, but not doing simulations\n",
+        "simulation_options = SimulationOptions(\n",
+        "    data_cast=DATA_CAST,\n",
+        "    data_recast=True,\n",
+        "    save_to_disk=True,\n",
+        "    input_filename=input_filename,\n",
+        "    output_filename=output_filename,\n",
+        "    save_to_disk_exit=False,\n",
+        "    data_path=DATA_PATH,\n",
+        "    pml_inside=False)\n",
+        "\n",
+        "execution_options = SimulationExecutionOptions(\n",
+        "    is_gpu_simulation=True,\n",
+        "    delete_data=False,\n",
+        "    verbose_level=2)\n",
+        "\n",
+        "sensor_data = kspaceFirstOrder3DG(\n",
+        "    medium=medium,\n",
+        "    kgrid=kgrid,\n",
+        "    source=source,\n",
+        "    sensor=sensor,\n",
+        "    simulation_options=simulation_options,\n",
+        "    execution_options=execution_options)"
+      ],
+      "metadata": {
+        "id": "eoJ0D_s3iOMV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ca1cd6e2-3478-46b8-caea-358d17b17e0a"
+      },
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Highest prime factors in each dimension are [13 13 47]\n",
+            "WARNING:root:Use dimension sizes with lower prime factors to improve speed\n",
+            "WARNING:root:DeprecationWarning: Attributes will soon be typed when saved and not saved \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "┌───────────────────────────────────────────────────────────────┐\n",
+            "│                  kspaceFirstOrder-CUDA v1.3                   │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Git hash:            468dc31c2842a7df5f2a07c3a13c16c9b0b2b770 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Reading simulation configuration:                        Done │\n",
+            "│ File format version:                                      1.2 │\n",
+            "│ Selected GPU device id:                                     0 │\n",
+            "│ GPU device name:                                     Tesla T4 │\n",
+            "│ Number of CPU threads:                                      2 │\n",
+            "│ Processor name:                Intel(R) Xeon(R) CPU @ 2.00GHz │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                      Simulation details                       │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Domain dimensions:                              91 x 91 x 141 │\n",
+            "│ Medium type:                                               3D │\n",
+            "│ Simulation time steps:                                   1004 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Input file:  ./bm7_1mm_input.h5                               │\n",
+            "│ Input file:  ./bm7_1mm_output.h5                              │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Compression level:                                          0 │\n",
+            "│ Print progress interval:                                   5% │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Medium details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Wave propagation:                                      Linear │\n",
+            "│ Absorption type:                                    Power law │\n",
+            "│ Medium parameters:                              Heterogeneous │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Source details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure source:                                              │\n",
+            "│ + Source type:                                           Many │\n",
+            "│ + Source condition:                                  Additive │\n",
+            "│ + Memory usage:                                       19.35MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Sensor details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sensor mask type:                                       Index │\n",
+            "│ Sampling begins at time step:                             915 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure sensor p_raw                                         │\n",
+            "│ + File usage:                                        209.41MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Initialization                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Memory allocation:                                       Done │\n",
+            "│ Data loading:                                                 │\n",
+            "│ + Reading input file:                                    Done │\n",
+            "│ + Creating output file:                                  Done │\n",
+            "│ Elapsed time:                                           0.07s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ FFT plans creation:                                      Done │\n",
+            "│ Pre-processing phase:                                    Done │\n",
+            "│ Elapsed time:                                           0.05s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                    Computational resources                    │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Current host memory in use:                             286MB │\n",
+            "│ Current device memory in use:                           329MB │\n",
+            "│ Expected output file size:                              209MB │\n",
+            "│ CUDA solver grid size [blocks x threads]:           320 x 256 │\n",
+            "│ CUDA sampler grid size [blocks x threads]:          320 x 256 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                          Simulation                           │\n",
+            "├──────────┬────────────────┬──────────────┬────────────────────┤\n",
+            "│ Progress │  Elapsed time  │  Time to go  │  Est. finish time  │\n",
+            "├──────────┼────────────────┼──────────────┼────────────────────┤\n",
+            "│     0%   │        0.001s  │      0.481s  │  29/02/24 09:24:26 │\n",
+            "│     5%   │        0.392s  │      7.173s  │  29/02/24 09:24:34 │\n",
+            "│    10%   │        0.757s  │      6.697s  │  29/02/24 09:24:33 │\n",
+            "│    15%   │        1.123s  │      6.294s  │  29/02/24 09:24:33 │\n",
+            "│    20%   │        1.492s  │      5.925s  │  29/02/24 09:24:33 │\n",
+            "│    25%   │        1.866s  │      5.538s  │  29/02/24 09:24:33 │\n",
+            "│    30%   │        2.233s  │      5.165s  │  29/02/24 09:24:34 │\n",
+            "│    35%   │        2.601s  │      4.797s  │  29/02/24 09:24:33 │\n",
+            "│    40%   │        2.967s  │      4.425s  │  29/02/24 09:24:33 │\n",
+            "│    45%   │        3.335s  │      4.057s  │  29/02/24 09:24:34 │\n",
+            "│    50%   │        3.711s  │      3.681s  │  29/02/24 09:24:33 │\n",
+            "│    55%   │        4.077s  │      3.312s  │  29/02/24 09:24:33 │\n",
+            "│    60%   │        4.448s  │      2.946s  │  29/02/24 09:24:33 │\n",
+            "│    65%   │        4.813s  │      2.576s  │  29/02/24 09:24:33 │\n",
+            "│    70%   │        5.180s  │      2.207s  │  29/02/24 09:24:34 │\n",
+            "│    75%   │        5.556s  │      1.832s  │  29/02/24 09:24:33 │\n",
+            "│    80%   │        5.923s  │      1.464s  │  29/02/24 09:24:33 │\n",
+            "│    85%   │        6.291s  │      1.096s  │  29/02/24 09:24:34 │\n",
+            "│    90%   │        6.659s  │      0.728s  │  29/02/24 09:24:33 │\n",
+            "│    95%   │        7.238s  │      0.371s  │  29/02/24 09:24:34 │\n",
+            "├──────────┴────────────────┴──────────────┴────────────────────┤\n",
+            "│ Elapsed time:                                           8.06s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sampled data post-processing:                            Done │\n",
+            "│ Elapsed time:                                           0.00s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                            Summary                            │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Peak host memory in use:                                291MB │\n",
+            "│ Peak device memory in use:                              329MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Total execution time:                                   8.63s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                       End of computation                      │\n",
+            "└───────────────────────────────────────────────────────────────┘\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Post-processing"
+      ],
+      "metadata": {
+        "id": "rZjseDsyWlQL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# sampling frequency\n",
+        "fs = 1.0 / kgrid.dt\n",
+        "\n",
+        "# get Fourier coefficients\n",
+        "amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1,\n",
+        "                              fft_padding=1, window='Rectangular')\n",
+        "\n",
+        "# reshape data: matlab uses Fortran ordering\n",
+        "p = np.reshape(amp, (Nx, Ny, Nz), order='F')\n",
+        "# get maximum pressure\n",
+        "pmax = np.nanmax(p)\n",
+        "# get location of maximum pressure\n",
+        "max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='F')\n",
+        "\n",
+        "p_water = np.empty_like(p)\n",
+        "p_water.fill(np.nan)\n",
+        "p_water[water_mask] = p[water_mask]\n",
+        "pmax_water = np.nanmax(p_water)\n",
+        "max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='F')\n",
+        "\n",
+        "p_skull = np.empty_like(p)\n",
+        "p_skull.fill(np.nan)\n",
+        "p_skull[skull_mask] = p[skull_mask]\n",
+        "pmax_skull = np.nanmax(p_skull)\n",
+        "max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='F')\n",
+        "\n",
+        "p_brain = np.empty_like(p)\n",
+        "p_brain.fill(np.nan)\n",
+        "p_brain[brain_mask] = p[brain_mask]\n",
+        "pmax_brain = np.nanmax(p_brain)\n",
+        "max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='F')\n",
+        "print(max_loc_brain)\n",
+        "#max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')\n",
+        "#print(max_loc_brain)\n",
+        "\n",
+        "# domain axes\n",
+        "x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)\n",
+        "y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)\n",
+        "z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)\n",
+        "\n",
+        "# colours\n",
+        "cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
+        "\n",
+        "# brain axes\n",
+        "# x\n",
+        "x_x_brain = [kgrid.z_vec[focus_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]\n",
+        "y_x_brain = [kgrid.x_vec[focus_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]\n",
+        "coefficients_x_brain = np.polyfit(x_x_brain, y_x_brain, 1)\n",
+        "polynomial_x_brain = np.poly1d(coefficients_x_brain)\n",
+        "beam_axis_x_brain = polynomial_x_brain(z_vec)\n",
+        "\n",
+        "# y\n",
+        "x_y_brain = [kgrid.z_vec[focus_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]\n",
+        "y_y_brain = [kgrid.y_vec[focus_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]\n",
+        "coefficients_y_brain = np.polyfit(x_y_brain, y_y_brain, 1)\n",
+        "polynomial_y_brain = np.poly1d(coefficients_y_brain)\n",
+        "beam_axis_y_brain = polynomial_y_brain(z_vec)\n",
+        "\n",
+        "# beam axis\n",
+        "beam_axis_brain = np.vstack((beam_axis_x_brain, beam_axis_y_brain, z_vec)).T\n",
+        "\n",
+        "# interpolate for pressure on brain axis\n",
+        "beam_pressure_brain = interpn((x_vec, y_vec, z_vec), p, beam_axis_brain,\n",
+        "                              method='linear', bounds_error=False, fill_value=np.nan)\n",
+        "\n",
+        "# skull axes\n",
+        "# x\n",
+        "x_x_skull = [kgrid.z_vec[focus_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]\n",
+        "y_x_skull = [kgrid.x_vec[focus_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]\n",
+        "coefficients_x_skull = np.polyfit(x_x_skull, y_x_skull, 1)\n",
+        "polynomial_x_skull = np.poly1d(coefficients_x_skull)\n",
+        "beam_axis_x_skull = polynomial_x_skull(z_vec)\n",
+        "\n",
+        "# y\n",
+        "x_y_skull = [kgrid.z_vec[focus_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]\n",
+        "y_y_skull = [kgrid.y_vec[focus_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]\n",
+        "coefficients_y_skull = np.polyfit(x_y_skull, y_y_skull, 1)\n",
+        "polynomial_y_skull = np.poly1d(coefficients_y_skull)\n",
+        "beam_axis_y_skull = polynomial_y_skull(z_vec)\n",
+        "\n",
+        "# beam axis\n",
+        "beam_axis_skull = np.vstack((beam_axis_x_skull, beam_axis_y_skull, z_vec)).T\n",
+        "\n",
+        "# interpolate for pressure\n",
+        "beam_pressure_skull = interpn((x_vec, y_vec, z_vec), p, beam_axis_skull,\n",
+        "                              method='linear', bounds_error=False,\n",
+        "                              fill_value=np.nan)\n",
+        "\n",
+        "# plot pressure on through centre lines\n",
+        "fig1, ax1 = plt.subplots()\n",
+        "ax1.plot(p[(Nx - 1) // 2, (Ny - 1) // 2, :] / 1e6, label='geometric focus')\n",
+        "ax1.plot(beam_pressure_brain / 1e6, label='brain focus')\n",
+        "ax1.plot(beam_pressure_skull / 1e6, label='skull focus')\n",
+        "ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)\n",
+        "ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)\n",
+        "ax1.set(xlabel='Axial Position [mm]',\n",
+        "        ylabel='Pressure [MPa]',\n",
+        "        title='Centreline Pressures')\n",
+        "ax1.legend()\n",
+        "ax1.grid(True)"
+      ],
+      "metadata": {
+        "id": "0n--X3UuiQ8z",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 408
+        },
+        "outputId": "32eac676-8f5e-4fba-8d6a-cbcaba7deb22"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(18, 28, 60)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/numpy/lib/polynomial.py:668: RuntimeWarning: invalid value encountered in divide\n",
+            "  lhs /= scale\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "LinAlgError",
+          "evalue": "SVD did not converge in Linear Least Squares",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-60-84f4e0b3621d>\u001b[0m in \u001b[0;36m<cell line: 48>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0mx_x_brain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz_vec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfocus_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz_vec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax_loc_brain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0my_x_brain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_vec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfocus_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_vec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax_loc_brain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0mcoefficients_x_brain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_x_brain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_x_brain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0mpolynomial_x_brain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoefficients_x_brain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0mbeam_axis_x_brain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpolynomial_x_brain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_vec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/polynomial.py\u001b[0m in \u001b[0;36mpolyfit\u001b[0;34m(x, y, deg, rcond, full, w, cov)\u001b[0m\n\u001b[1;32m    667\u001b[0m     \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlhs\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlhs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m     \u001b[0mlhs\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m     \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlhs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrhs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrcond\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m  \u001b[0;31m# broadcast scale coefficients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, rcond)\u001b[0m\n\u001b[1;32m   2324\u001b[0m         \u001b[0;31m# lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2325\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rhs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2326\u001b[0;31m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2327\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2328\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36m_raise_linalgerror_lstsq\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_lstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SVD did not converge in Linear Least Squares\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_qr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mLinAlgError\u001b[0m: SVD did not converge in Linear Least Squares"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_edges(mask, fill_with_nan=True):\n",
+        "    \"\"\"returns the mask as a float array and np.nan\"\"\"\n",
+        "    edges = find_boundaries(mask, mode='thin').astype(np.float32)\n",
+        "    if fill_with_nan:\n",
+        "        edges[edges == 0] = np.nan\n",
+        "    return edges\n",
+        "\n",
+        "# contouring block\n",
+        "edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int),\n",
+        "                    fill_with_nan=False)\n",
+        "\n",
+        "contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)\n",
+        "contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)\n",
+        "contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Ny\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_x):\n",
+        "    j = np.argmax(np.where(contour_x[:, Nz // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_x[:, Nz // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "contours_x_inner = measure.find_contours(np.where(contour_x == i_inner, 1, 0))\n",
+        "if not contours_x_inner:\n",
+        "    print(\"size of contours_x_inner is zero\")\n",
+        "\n",
+        "contours_x_outer = measure.find_contours(np.where(contour_x == i_outer, 1, 0))\n",
+        "if not contours_x_outer:\n",
+        "   print(\"size of contours_x_outer is zero\")\n",
+        "\n",
+        "inner_index_x = float(Ny)\n",
+        "outer_index_x = float(0)\n",
+        "for i in range(len(contours_x_inner)):\n",
+        "    x_min = np.min(contours_x_inner[i][:, 1])\n",
+        "    if (x_min < inner_index_x):\n",
+        "        inner_index_x = i\n",
+        "for i in range(len(contours_x_outer)):\n",
+        "    x_max = np.max(contours_x_outer[i][:, 1])\n",
+        "    if (x_max > outer_index_x):\n",
+        "        outer_index_x = i\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Nx\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_y):\n",
+        "    j = np.argmax(np.where(contour_y[:, Nz // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_y[:, Nz // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "contours_y_inner = measure.find_contours(np.where(contour_y == i_inner, 1, 0))\n",
+        "if not contours_y_inner:\n",
+        "    print(\"size of contours_y_inner is zero\")\n",
+        "contours_y_outer = measure.find_contours(np.where(contour_y == i_outer, 1, 0))\n",
+        "if not contours_y_outer:\n",
+        "    print(\"size of contours_y_outer is zero\")\n",
+        "inner_index_y = float(Nx)\n",
+        "outer_index_y = float(0)\n",
+        "for i in range(len(contours_y_inner)):\n",
+        "    y_min = np.min(contours_y_inner[i][:, 1])\n",
+        "    if (y_min < inner_index_y):\n",
+        "        inner_index_y = i\n",
+        "for i in range(len(contours_y_outer)):\n",
+        "    y_max = np.max(contours_y_outer[i][:, 1])\n",
+        "    if (y_max > outer_index_y):\n",
+        "        outer_index_y = i\n",
+        "\n",
+        "jmax = 0\n",
+        "jmin = Ny\n",
+        "i_inner = None\n",
+        "i_outer = None\n",
+        "for i in range(num_z):\n",
+        "    j = np.argmax(np.where(contour_z[:, Nx // 2] == (i + 1), 1, 0))\n",
+        "    if (j > jmax):\n",
+        "        jmax = j\n",
+        "        i_outer = i + 1\n",
+        "    k = np.argmin(np.where(contour_z[:, Nx // 2] == (i + 1), 0, 1))\n",
+        "    if (k < jmin):\n",
+        "        jmin = k\n",
+        "        i_inner = i + 1\n",
+        "\n",
+        "contours_z_inner = measure.find_contours(np.where(contour_z == i_inner, 1, 0))\n",
+        "if not contours_z_inner:\n",
+        "    pass\n",
+        "else:\n",
+        "    inner_index_z = float(Nx)\n",
+        "    for i in range(len(contours_z_inner)):\n",
+        "        z_min = np.min(contours_z_inner[i][:, 1])\n",
+        "        if (z_min < inner_index_z):\n",
+        "            inner_index_z = i\n",
+        "\n",
+        "contours_z_outer = measure.find_contours(np.where(contour_z == i_outer, 1, 0))\n",
+        "if not contours_z_outer:\n",
+        "    pass\n",
+        "else:\n",
+        "    outer_index_z = float(0)\n",
+        "    for i in range(len(contours_z_outer)):\n",
+        "        z_max = np.max(contours_z_outer[i][:, 1])\n",
+        "        if (z_max > outer_index_z):\n",
+        "            outer_index_z = i\n",
+        "\n",
+        "# end of contouring block\n",
+        "edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))\n",
+        "edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))\n",
+        "edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int))\n",
+        "\n",
+        "# plot the pressure field at mid point along z axis\n",
+        "fig2, ax2 = plt.subplots()\n",
+        "im2 = ax2.pcolormesh(x_vec, y_vec, p[:, :, max_loc_brain[2]] / 1e6,\n",
+        "                     shading='gouraud',\n",
+        "                     cmap='viridis')\n",
+        "\n",
+        "if not contours_z_inner:\n",
+        "    ax2.pcolormesh(x_vec, y_vec, edges_z, shading='gouraud',\n",
+        "               cmap='Greys')\n",
+        "else:\n",
+        "    ax2.plot(contours_z_inner[inner_index_z][:, 1],\n",
+        "             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)\n",
+        "if not contours_z_outer:\n",
+        "    pass\n",
+        "else:\n",
+        "    ax2.plot(contours_z_outer[outer_index_z][:, 1],\n",
+        "             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)\n",
+        "#\n",
+        "ax2.set(xlabel=r'$x$ [mm]',\n",
+        "        ylabel=r'$y$ [mm]',\n",
+        "        title='Pressure Field')\n",
+        "ax2.grid(False)\n",
+        "divider2 = make_axes_locatable(ax2)\n",
+        "cax2 = divider2.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_2 = fig2.colorbar(im2, cax=cax2)\n",
+        "_ = cbar_2.ax.set_title('[MPa]', fontsize='small')"
+      ],
+      "metadata": {
+        "id": "ZvrJDRhJiXKg",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        },
+        "outputId": "9388821e-5f57-465f-efc7-523f0949162d"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig3, (ax3a, ax3b) = plt.subplots(1, 2)\n",
+        "\n",
+        "im3a = ax3a.pcolormesh(np.squeeze(kgrid.y_vec), np.squeeze(kgrid.z_vec), p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "\n",
+        "ax3a.plot(beam_axis_y_skull, np.squeeze(kgrid.z_vec), 'w--', lw=0.5)\n",
+        "ax3a.plot(beam_axis_y_brain, np.squeeze(kgrid.z_vec), 'w--', lw=0.5)\n",
+        "\n",
+        "# reversed and new underscore\n",
+        "ax3a.scatter(y_y_skull, x_y_skull, c='w', marker='o', edgecolors='none', alpha=0.5)\n",
+        "ax3a.scatter(y_y_brain, x_y_brain, c='w', marker='+', alpha=0.5)\n",
+        "\n",
+        "ax3a.plot(contours_x_inner[outer_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],\n",
+        "          contours_x_inner[outer_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "ax3a.plot(contours_x_outer[inner_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],\n",
+        "          contours_x_outer[inner_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "\n",
+        "ax3a.grid(False)\n",
+        "ax3a.axes.get_yaxis().set_visible(False)\n",
+        "ax3a.axes.get_xaxis().set_visible(False)\n",
+        "divider3a = make_axes_locatable(ax3a)\n",
+        "cax3a = divider3a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3a = fig3.colorbar(im3a, cax=cax3a)\n",
+        "cbar_3a.ax.set_title('[MPa]', fontsize='small')\n",
+        "ax3a.invert_yaxis()\n",
+        "\n",
+        "im3b = ax3b.pcolormesh(np.squeeze(kgrid.x_vec), np.squeeze(kgrid.z_vec), p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "\n",
+        "ax3b.plot(beam_axis_x_skull, np.squeeze(kgrid.z_vec), 'w--', lw=0.5)\n",
+        "ax3b.plot(beam_axis_x_brain, np.squeeze(kgrid.z_vec), 'w--', lw=0.5)\n",
+        "\n",
+        "ax3b.scatter(y_x_skull, x_x_skull, c='w', marker='o', edgecolors='none', alpha=0.5)\n",
+        "ax3b.scatter(y_x_brain, x_x_brain, c='w', marker='+', alpha=0.5)\n",
+        "\n",
+        "ax3b.plot(contours_y_inner[inner_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],\n",
+        "          contours_y_inner[inner_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "ax3b.plot(contours_y_outer[outer_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],\n",
+        "          contours_y_outer[outer_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],\n",
+        "          'w', linewidth=0.5)\n",
+        "\n",
+        "ax3b.grid(False)\n",
+        "ax3b.axes.get_yaxis().set_visible(False)\n",
+        "ax3b.axes.get_xaxis().set_visible(False)\n",
+        "divider3b = make_axes_locatable(ax3b)\n",
+        "cax3b = divider3b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3b = fig3.colorbar(im3a, cax=cax3b)\n",
+        "cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})\n",
+        "ax3b.invert_yaxis()\n",
+        "plt.tight_layout(pad=1.2)"
+      ],
+      "metadata": {
+        "id": "cADW_Na2id87",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "ab5dc1c2-e31c-4a4f-d3ad-24c8265b34b6"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(18, 28, 60)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/examples/benchmarks/7/ph1-bm7-sc2.py b/examples/benchmarks/7/ph1-bm7-sc2.py
new file mode 100644
index 000000000..def1e1c0a
--- /dev/null
+++ b/examples/benchmarks/7/ph1-bm7-sc2.py
@@ -0,0 +1,720 @@
+import numpy as np
+
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+from matplotlib.colors import Normalize
+import matplotlib.cm as cm
+
+from cycler import cycler
+
+import h5py
+
+from skimage import measure
+from skimage.segmentation import find_boundaries
+from scipy.interpolate import interpn
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+verbose: bool = True
+savePlotting: bool = False
+useMaxTimeStep: bool = True
+
+tag = 'bm7'
+res = '1mm'
+
+mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'
+
+data = h5py.File(mask_filename, 'r')
+
+# is given in millimetres
+dx = data['dx'][:].item()
+
+# scale to metres
+dx = dx / 1000.0
+dy = dx
+dz = dx
+
+xi = np.squeeze(np.asarray(data['xi'][:]))
+yi = np.squeeze(np.asarray(data['yi'][:]))
+zi = np.squeeze(np.asarray(data['zi'][:]))
+
+matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]
+
+skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)
+brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)
+
+skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')
+brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')
+
+water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))
+water_mask = water_mask.astype(bool)
+
+skull_mask = np.swapaxes(skull_mask, 0, 2)
+brain_mask = np.swapaxes(brain_mask, 0, 2)
+water_mask = np.swapaxes(water_mask, 0, 2)
+
+Nx, Ny, Nz = skull_mask.shape
+
+focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, (Nz - 1) // 2]
+
+disc_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones(skull_mask.shape)
+density = 1000.0 * np.ones(skull_mask.shape)
+alpha_coeff = np.zeros(skull_mask.shape)
+
+# non-dispersive
+alpha_power = 2.0
+
+# skull
+sound_speed[skull_mask] = 2800.0
+density[skull_mask] = 1850.0
+alpha_coeff[skull_mask] = 4.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(sound_speed.flatten())
+c0_max = np.min(sound_speed.flatten())
+
+medium = kWaveMedium(sound_speed=sound_speed,
+                     density=density,
+                     alpha_coeff=alpha_coeff,
+                     alpha_power=alpha_power)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# diameter of the disc
+diameter = 20e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min: float = c0_min / freq
+
+# points per wavelength
+ppw: float = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 30
+
+# CFL number determines time step
+cfl: float = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Ny, Nz])
+
+grid_spacing_meters = Vector([dx, dy, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil(1.0 / (dt_stability_limit * freq))
+    dt = 1.0 / (ppp * freq)
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set disc position
+position = [kgrid.x_vec[disc_coords[0]].item(),
+            kgrid.y_vec[disc_coords[1]].item(),
+            kgrid.z_vec[disc_coords[2]].item()]
+
+# arbitrary position
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item(),
+             kgrid.z_vec[focus_coords[2]].item()]
+
+# add disc-shaped planar element
+karray.add_disc_element(position, diameter, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = 'data/'
+
+input_filename = tag + '_' + res + '_input.h5'
+output_filename = tag + '_' + res + '_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspaceFirstOrder3DG(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# POST-PROCESS
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')
+
+# reshape data: matlab uses Fortran ordering
+p = np.reshape(amp, (Nx, Ny, Nz), order='F')
+pmax = np.nanmax(p)
+max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='C')
+
+p_water = np.empty_like(p)
+p_water.fill(np.nan)
+p_water[water_mask] = p[water_mask]
+pmax_water = np.nanmax(p_water)
+max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='C')
+
+p_skull = np.empty_like(p)
+p_skull.fill(np.nan)
+p_skull[skull_mask] = p[skull_mask]
+pmax_skull = np.nanmax(p_skull)
+max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='C')
+
+p_brain = np.empty_like(p)
+p_brain.fill(np.nan)
+p_brain[brain_mask] = p[brain_mask]
+pmax_brain = np.nanmax(p_brain)
+max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')
+
+# domain axes
+x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)
+y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)
+z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+
+# colours
+cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
+
+# brain axes
+# x
+x_x_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_x_brain = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]
+coefficients_x_brain = np.polyfit(x_x_brain, y_x_brain, 1)
+polynomial_x_brain = np.poly1d(coefficients_x_brain)
+beam_axis_x_brain = polynomial_x_brain(z_vec)
+# y
+x_y_brain = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_y_brain = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]
+coefficients_y_brain = np.polyfit(x_y_brain, y_y_brain, 1)
+polynomial_y_brain = np.poly1d(coefficients_y_brain)
+beam_axis_y_brain = polynomial_y_brain(z_vec)
+# beam axis
+beam_axis_brain = np.vstack((beam_axis_x_brain, beam_axis_y_brain, z_vec)).T
+# interpolate for pressure on brain axis
+beam_pressure_brain = interpn((x_vec, y_vec, z_vec), p, beam_axis_brain,
+                              method='linear', bounds_error=False, fill_value=np.nan)
+
+# skull axes
+# x
+x_x_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_x_skull = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]
+coefficients_x_skull = np.polyfit(x_x_skull, y_x_skull, 1)
+polynomial_x_skull = np.poly1d(coefficients_x_skull)
+beam_axis_x_skull = polynomial_x_skull(z_vec)
+# y
+x_y_skull = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_y_skull = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]
+coefficients_y_skull = np.polyfit(x_y_skull, y_y_skull, 1)
+polynomial_y_skull = np.poly1d(coefficients_y_skull)
+beam_axis_y_skull = polynomial_y_skull(z_vec)
+# beam axis
+beam_axis_skull = np.vstack((beam_axis_x_skull, beam_axis_y_skull, z_vec)).T
+# interpolate for pressure
+beam_pressure_skull = interpn((x_vec, y_vec, z_vec), p, beam_axis_skull,
+                              method='linear', bounds_error=False,
+                              fill_value=np.nan)
+
+# plot pressure on through centre lines
+fig1, ax1 = plt.subplots()
+ax1.plot(p[(Nx - 1) // 2, (Ny - 1) // 2, :] / 1e6, label='geometric')
+ax1.plot(beam_pressure_brain / 1e6, label='focal')
+ax1.plot(beam_pressure_skull / 1e6, label='skull')
+ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)
+ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)
+ax1.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [MPa]',
+        title='Centreline Pressures')
+ax1.legend()
+ax1.grid(True)
+
+
+
+def get_edges(mask, fill_with_nan=True):
+    """returns the mask as a float array and np.NaN"""
+    edges = find_boundaries(mask, mode='thin').astype(np.float32)
+    if fill_with_nan:
+        edges[edges == 0] = np.nan
+    return edges
+
+
+# contouring block
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int),
+                    fill_with_nan=False)
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int),
+                    fill_with_nan=False)
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int),
+                    fill_with_nan=False)
+
+contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)
+contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)
+contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_x):
+    j = np.argmax(np.where(contour_x[:, Nz // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_x[:, Nz // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+contours_x_inner = measure.find_contours(np.where(contour_x == i_inner, 1, 0))
+if not contours_x_inner:
+    pass
+contours_x_outer = measure.find_contours(np.where(contour_x == i_outer, 1, 0))
+if not contours_x_outer:
+   pass
+inner_index_x = float(Ny)
+outer_index_x = float(0)
+for i in range(len(contours_x_inner)):
+    x_min = np.min(contours_x_inner[i][:, 1])
+    if (x_min < inner_index_x):
+        inner_index_x = i
+for i in range(len(contours_x_outer)):
+    x_max = np.max(contours_x_outer[i][:, 1])
+    if (x_max > outer_index_x):
+        outer_index_x = i
+
+jmax = 0
+jmin = Nx
+i_inner = None
+i_outer = None
+for i in range(num_y):
+    j = np.argmax(np.where(contour_y[:, Nz // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_y[:, Nz // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+contours_y_inner = measure.find_contours(np.where(contour_y == i_inner, 1, 0))
+if not contours_y_inner:
+    pass
+contours_y_outer = measure.find_contours(np.where(contour_y == i_outer, 1, 0))
+if not contours_y_outer:
+    pass
+inner_index_y = float(Nx)
+outer_index_y = float(0)
+for i in range(len(contours_y_inner)):
+    y_min = np.min(contours_y_inner[i][:, 1])
+    if (y_min < inner_index_y):
+        inner_index_y = i
+for i in range(len(contours_y_outer)):
+    y_max = np.max(contours_y_outer[i][:, 1])
+    if (y_max > outer_index_y):
+        outer_index_y = i
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_z):
+    j = np.argmax(np.where(contour_z[:, Nx // 2] == (i + 1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i + 1
+    k = np.argmin(np.where(contour_z[:, Nx // 2] == (i + 1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i + 1
+
+contours_z_inner = measure.find_contours(np.where(contour_z == i_inner, 1, 0))
+if not contours_z_inner:
+    pass
+else:
+    inner_index_z = float(Nx)
+    for i in range(len(contours_z_inner)):
+        z_min = np.min(contours_z_inner[i][:, 1])
+        if (z_min < inner_index_z):
+            inner_index_z = i
+
+contours_z_outer = measure.find_contours(np.where(contour_z == i_outer, 1, 0))
+if not contours_z_outer:
+    pass
+else:
+    outer_index_z = float(0)
+    for i in range(len(contours_z_outer)):
+        z_max = np.max(contours_z_outer[i][:, 1])
+        if (z_max > outer_index_z):
+            outer_index_z = i
+
+# end of contouring block
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int))
+
+# plot the pressure field at mid point along z axis
+fig2, ax2 = plt.subplots()
+im2 = ax2.imshow(p[:, :, max_loc_brain[2]] / 1e6,
+                 aspect='auto',
+                 interpolation='none',
+                 origin='lower',
+                 cmap='viridis')
+
+if not contours_z_inner:
+    ax2.imshow(edges_z, aspect='auto', interpolation='none',
+               cmap='Greys', origin='upper')
+else:
+    ax2.plot(contours_z_inner[inner_index_z][:, 1],
+             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax2.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+#
+ax2.set(xlabel=r'$x$ [mm]',
+        ylabel=r'$y$ [mm]',
+        title='Pressure Field')
+ax2.grid(False)
+divider2 = make_axes_locatable(ax2)
+cax2 = divider2.append_axes("right", size="5%", pad=0.05)
+cbar_2 = fig2.colorbar(im2, cax=cax2)
+cbar_2.ax.set_title('[MPa]', fontsize='small')
+
+pwater_max_x = np.nanmax(p_water[max_loc_brain[0], :, :].flatten())
+pskull_max_x = np.nanmax(p_skull[max_loc_brain[0], :, :].flatten())
+pbrain_max_x = np.nanmax(p_brain[max_loc_brain[0], :, :].flatten())
+
+pwater_max_y = np.nanmax(p_water[:, max_loc_brain[1], :].flatten())
+pskull_max_y = np.nanmax(p_skull[:, max_loc_brain[1], :].flatten())
+pbrain_max_y = np.nanmax(p_brain[:, max_loc_brain[1], :].flatten())
+
+px_vec = np.linspace(kgrid.x_vec[0].item() - kgrid.dx,
+                     kgrid.x_vec[-1].item() + kgrid.dx, kgrid.Nx + 1)
+py_vec = np.linspace(kgrid.y_vec[0].item() - kgrid.dy,
+                     kgrid.y_vec[-1].item() + kgrid.dy, kgrid.Ny + 1)
+pz_vec = np.linspace(kgrid.z_vec[0].item() - kgrid.dz,
+                     kgrid.z_vec[-1].item() + kgrid.dz, kgrid.Nz + 1)
+
+fig3, (ax3a, ax3b) = plt.subplots(1, 2)
+im3a = ax3a.pcolormesh(y_vec, z_vec, p[max_loc_brain[0], :, :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+
+ax3a.plot(beam_axis_y_skull, z_vec, 'w--', lw=0.5)
+ax3a.plot(beam_axis_y_brain, z_vec, 'w--', lw=0.5)
+
+# reversed and new underscore
+ax3a.scatter(y_y_skull, x_y_skull, c='w', marker='o', edgecolors='none', alpha=0.5)
+ax3a.scatter(y_y_brain, x_y_brain, c='w', marker='+', alpha=0.5)
+
+# ------------------------------------------------------------------------------
+ax3a.plot(contours_x_inner[outer_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],
+          contours_x_inner[outer_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+ax3a.plot(contours_x_outer[inner_index_x][:, 1] * kgrid.dy + kgrid.y_vec[0],
+          contours_x_outer[inner_index_x][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+# ------------------------------------------------------------------------------
+ax3a.grid(False)
+ax3a.axes.get_yaxis().set_visible(False)
+ax3a.axes.get_xaxis().set_visible(False)
+divider3a = make_axes_locatable(ax3a)
+cax3a = divider3a.append_axes("right", size="5%", pad=0.05)
+cbar_3a = fig3.colorbar(im3a, cax=cax3a)
+cbar_3a.ax.set_title('[MPa]', fontsize='small')
+
+ax3a.invert_yaxis()
+
+im3b = ax3b.pcolormesh(x_vec, z_vec, p[:, max_loc_brain[1], :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+
+# ------------------------------------------------------------------------------
+ax3b.plot(contours_y_inner[inner_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],
+          contours_y_inner[inner_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+ax3b.plot(contours_y_outer[outer_index_y][:, 1] * kgrid.dx + kgrid.x_vec[0],
+          contours_y_outer[outer_index_y][:, 0] * kgrid.dz + kgrid.z_vec[0],
+          'w', linewidth=0.5)
+
+ax3b.plot(beam_axis_x_skull, z_vec, 'w--', lw=0.5)
+ax3b.plot(beam_axis_x_brain, z_vec, 'w--', lw=0.5)
+ax3b.scatter(y_x_skull, x_x_skull, c='w', marker='o', edgecolors='none', alpha=0.5)
+ax3b.scatter(y_x_brain, x_x_brain, c='w', marker='+', alpha=0.5)
+
+# ------------------------------------------------------------------------------
+ax3b.grid(False)
+ax3b.axes.get_yaxis().set_visible(False)
+ax3b.axes.get_xaxis().set_visible(False)
+divider3b = make_axes_locatable(ax3b)
+cax3b = divider3b.append_axes("right", size="5%", pad=0.05)
+cbar_3b = fig3.colorbar(im3b, cax=cax3b)
+cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})
+ax3b.invert_yaxis()
+
+fig4, ax4 = plt.subplots(1, 2)
+(ax4a, ax4b) = ax4
+cmap = cm.viridis
+normalizer = Normalize(vmin=0, vmax=pmax / 1e6, clip=False)
+im = cm.ScalarMappable(norm=normalizer, cmap='viridis')
+
+p_source_index = np.squeeze(
+    np.array(h5py.File(DATA_PATH + input_filename, mode='r')["p_source_index"]))
+
+pml_x_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_x_size"]))
+pml_x_size = pml_x_size[()]
+pml_y_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_y_size"]))
+pml_y_size = pml_y_size[()]
+pml_z_size = np.squeeze(np.array(h5py.File(DATA_PATH + input_filename, mode='r')["pml_z_size"]))
+pml_z_size = pml_z_size[()]
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Nx + pml_x_size, source.p_mask.shape[0], dtype=int), 0)
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Ny + pml_y_size, source.p_mask.shape[1], dtype=int), 1)
+
+source.p_mask = np.delete(source.p_mask,
+                          np.arange(Nz + pml_z_size, source.p_mask.shape[2], dtype=int), 2)
+
+
+source.p_mask.astype(float)
+
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_x_size, dtype=int), 0)
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_y_size, dtype=int), 1)
+source.p_mask = np.delete(source.p_mask, np.arange(0, pml_z_size, dtype=int), 2)
+
+tx = np.empty_like(p)
+tx.fill(np.nan)
+tx.flatten()[p_source_index] = 1
+tx.reshape(p.shape, order='F')
+
+im4a = ax4a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap=cmap)
+
+im4a_boundary = ax4a.imshow(edges_x, aspect='auto',
+                            interpolation='none', origin='upper', cmap='Greys')
+
+fig5, (ax5a, ax5b) = plt.subplots(1, 2)
+im5a = ax5a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5a_boundary = ax5a.imshow(edges_x, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5a.grid(False)
+ax5a.axes.get_yaxis().set_visible(False)
+ax5a.axes.get_xaxis().set_visible(False)
+divider5a = make_axes_locatable(ax5a)
+cax5a = divider5a.append_axes("right", size="5%", pad=0.05)
+cbar_5a = fig5.colorbar(im5a, cax=cax5a)
+cbar_5a.ax.set_title('[MPa]', fontsize='small')
+im5b = ax5b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5b_boundary = ax5b.imshow(edges_y, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5b.grid(False)
+ax5b.axes.get_yaxis().set_visible(False)
+ax5b.axes.get_xaxis().set_visible(False)
+divider5b = make_axes_locatable(ax5b)
+cax5b = divider5b.append_axes("right", size="5%", pad=0.05)
+cbar_5b = fig5.colorbar(im5b, cax=cax5b)
+cbar_5b.ax.set_title('[MPa]', fontsize='small')
+
+all_contours_x = []
+for i in range(num_x):
+    all_contours_x.append(measure.find_contours(np.where(contour_x == (i + 1), 1, 0)))
+
+all_contours_y = []
+for i in range(num_y):
+    all_contours_y.append(measure.find_contours(np.where(contour_y == (i + 1), 1, 0)))
+
+fig6, (ax6a, ax6b) = plt.subplots(1, 2)
+im6a = ax6a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+for contour in all_contours_x:
+    for i in range(len(contour)):
+        ax6a.plot(contour[i][:, 1], contour[i][:, 0], 'w', linewidth=0.5)
+ax6a.grid(False)
+ax6a.axes.get_yaxis().set_visible(False)
+ax6a.axes.get_xaxis().set_visible(False)
+divider6a = make_axes_locatable(ax5a)
+cax6a = divider6a.append_axes("right", size="5%", pad=0.05)
+cbar_6a = fig6.colorbar(im6a, cax=cax6a)
+cbar_6a.ax.set_title('[MPa]', fontsize='small')
+im6b = ax6b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+
+custom_cycler = cycler(ls=['-', '--', ':', '-.'])
+
+ax6b.set_prop_cycle(custom_cycler)
+
+for idx, contour in enumerate(all_contours_y):
+    for i in range(len(contour)):
+        ax6b.plot(contour[i][:, 1], contour[i][:, 0], c=cycle[idx],
+                  linewidth=0.5, label=str(idx))
+ax6b.legend()
+ax6b.grid(False)
+ax6b.axes.get_yaxis().set_visible(False)
+ax6b.axes.get_xaxis().set_visible(False)
+divider6b = make_axes_locatable(ax6b)
+cax6b = divider6b.append_axes("right", size="5%", pad=0.05)
+cbar_6b = fig6.colorbar(im6b, cax=cax6b)
+cbar_6b.ax.set_title('[MPa]', fontsize='small')
+
+plt.show()
diff --git a/examples/benchmarks/7/skull_mask_bm7_dx_1mm.mat b/examples/benchmarks/7/skull_mask_bm7_dx_1mm.mat
new file mode 100644
index 000000000..95c0483ee
Binary files /dev/null and b/examples/benchmarks/7/skull_mask_bm7_dx_1mm.mat differ
diff --git a/examples/benchmarks/8/ph1-bm8-sc1.ipynb b/examples/benchmarks/8/ph1-bm8-sc1.ipynb
new file mode 100644
index 000000000..94c93f52d
--- /dev/null
+++ b/examples/benchmarks/8/ph1-bm8-sc1.ipynb
@@ -0,0 +1,623 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyP6dyWft2iQlERApxZtnk2X",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/djps/k-wave-python/blob/benchmarks/examples/benchmarks/8/ph1-bm8-sc1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Tke5dl-DsUsg",
+        "outputId": "a1481320-f31c-4a42-f01a-8623adcd77ab"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/waltsims/k-wave-python\n",
+            "  Cloning https://github.com/waltsims/k-wave-python to /tmp/pip-req-build-9jon34lv\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/waltsims/k-wave-python /tmp/pip-req-build-9jon34lv\n",
+            "  Resolved https://github.com/waltsims/k-wave-python to commit d8f56f34eea9d1e68f72bce3708a9fc55f2fb71f\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: beartype==0.16.4 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (0.16.4)\n",
+            "Requirement already satisfied: deepdiff==6.7.1 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (6.7.1)\n",
+            "Requirement already satisfied: h5py==3.10.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.10.0)\n",
+            "Requirement already satisfied: matplotlib==3.8.3 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.8.3)\n",
+            "Requirement already satisfied: nptyping==2.5.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (2.5.0)\n",
+            "Requirement already satisfied: numpy<1.27.0,>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.25.2)\n",
+            "Requirement already satisfied: opencv-python==4.9.0.80 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (4.9.0.80)\n",
+            "Requirement already satisfied: scipy==1.12.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.12.0)\n",
+            "Requirement already satisfied: ordered-set<4.2.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from deepdiff==6.7.1->k-Wave-python==0.3.2) (4.1.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (4.49.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (23.2)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (2.8.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib==3.8.3->k-Wave-python==0.3.2) (1.16.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/waltsims/k-wave-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from copy import deepcopy\n",
+        "import requests\n",
+        "import shutil\n",
+        "\n",
+        "import logging\n",
+        "import sys\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "from cycler import cycler\n",
+        "\n",
+        "import h5py\n",
+        "\n",
+        "from skimage import measure\n",
+        "from skimage.segmentation import find_boundaries\n",
+        "from scipy.interpolate import interpn\n",
+        "\n",
+        "from kwave.data import Vector\n",
+        "from kwave.utils.kwave_array import kWaveArray\n",
+        "from kwave.utils.checks import check_stability\n",
+        "from kwave.kgrid import kWaveGrid\n",
+        "from kwave.kmedium import kWaveMedium\n",
+        "from kwave.ksource import kSource\n",
+        "from kwave.ksensor import kSensor\n",
+        "from kwave.utils.signals import create_cw_signals\n",
+        "from kwave.utils.filters import extract_amp_phase\n",
+        "from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG\n",
+        "\n",
+        "from kwave.options.simulation_options import SimulationOptions\n",
+        "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+        "\n",
+        "# create logger\n",
+        "logger = logging.getLogger(__name__)\n",
+        "logger.setLevel(logging.DEBUG)\n",
+        "# create console and file handlers and set level to debug\n",
+        "ch = logging.StreamHandler(sys.stdout)\n",
+        "ch.setLevel(logging.DEBUG)\n",
+        "fh = logging.FileHandler(filename='runner.log')\n",
+        "fh.setLevel(logging.DEBUG)\n",
+        "# create formatter\n",
+        "formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')\n",
+        "# add formatter to ch, fh\n",
+        "ch.setFormatter(formatter)\n",
+        "fh.setFormatter(formatter)\n",
+        "# add ch, fh to logger\n",
+        "logger.addHandler(ch)\n",
+        "logger.addHandler(fh)\n",
+        "# propagate\n",
+        "ch.propagate = True\n",
+        "fh.propagate = True\n",
+        "logger.propagate = True\n",
+        "\n",
+        "verbose: bool = True\n",
+        "savePlotting: bool = True\n",
+        "useMaxTimeStep: bool = True\n",
+        "\n",
+        "tag = 'bm8'\n",
+        "res = '1mm'\n",
+        "\n",
+        "url = 'https://raw.githubusercontent.com/djps/k-wave-python/benchmarks/examples/benchmarks/8/skull_mask_bm8_dx_1mm.mat'\n",
+        "mask_filename = requests.get(url, stream=True)\n",
+        "mask_filename.raw.decode_content = True\n",
+        "with open(\"temp.h5\", \"wb\") as _fh:\n",
+        "    shutil.copyfileobj(mask_filename.raw, _fh)\n",
+        "\n",
+        "data = h5py.File(\"temp.h5\", 'r')\n",
+        "\n",
+        "# is given in millimetres\n",
+        "dx = data['dx'][:].item()\n",
+        "\n",
+        "# scale to metres\n",
+        "dx = dx / 1000.0\n",
+        "dy = dx\n",
+        "dz = dx\n",
+        "\n",
+        "xi = np.squeeze(np.asarray(data['xi'][:]))\n",
+        "yi = np.squeeze(np.asarray(data['yi'][:]))\n",
+        "zi = np.squeeze(np.asarray(data['zi'][:]))\n",
+        "\n",
+        "matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]\n",
+        "\n",
+        "skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)\n",
+        "brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)\n",
+        "\n",
+        "skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')\n",
+        "brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')\n",
+        "\n",
+        "water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))\n",
+        "water_mask = water_mask.astype(bool)\n",
+        "\n",
+        "skull_mask = np.swapaxes(skull_mask, 0, 2)\n",
+        "brain_mask = np.swapaxes(brain_mask, 0, 2)\n",
+        "water_mask = np.swapaxes(water_mask, 0, 2)\n",
+        "\n",
+        "Nx, Ny, Nz = skull_mask.shape\n",
+        "\n",
+        "focus = int(64 / data['dx'][:].item())\n",
+        "\n",
+        "focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, focus]\n",
+        "\n",
+        "bowl_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]\n",
+        "\n",
+        "disc_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]"
+      ],
+      "metadata": {
+        "id": "8yA1bMzLsafy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE MATERIAL PROPERTIES\n",
+        "# =========================================================================\n",
+        "\n",
+        "# water\n",
+        "sound_speed = 1500.0 * np.ones(skull_mask.shape)\n",
+        "density = 1000.0 * np.ones(skull_mask.shape)\n",
+        "alpha_coeff = np.zeros(skull_mask.shape)\n",
+        "\n",
+        "# non-dispersive\n",
+        "alpha_power = 2.0\n",
+        "\n",
+        "# skull\n",
+        "sound_speed[skull_mask] = 2800.0\n",
+        "density[skull_mask] = 1850.0\n",
+        "alpha_coeff[skull_mask] = 4.0\n",
+        "\n",
+        "# brain\n",
+        "sound_speed[brain_mask] = 1560.0\n",
+        "density[brain_mask] = 1040.0\n",
+        "alpha_coeff[brain_mask] = 0.3\n",
+        "\n",
+        "c0_min = np.min(sound_speed.flatten())\n",
+        "c0_max = np.min(sound_speed.flatten())\n",
+        "\n",
+        "medium = kWaveMedium(\n",
+        "    sound_speed=sound_speed,\n",
+        "    density=density,\n",
+        "    alpha_coeff=alpha_coeff,\n",
+        "    alpha_power=alpha_power\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "oD3h4kmosyU7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# bowl radius of curvature [m]\n",
+        "source_roc = 64.0e-3\n",
+        "\n",
+        "# as we will use the bowl element this has to be a int or float\n",
+        "diameters = 64.0e-3\n",
+        "\n",
+        "# frequency [Hz]\n",
+        "freq = 500e3\n",
+        "\n",
+        "# source pressure [Pa]\n",
+        "source_amp = np.array([60e3])\n",
+        "\n",
+        "# phase [rad]\n",
+        "source_phase = np.array([0.0])"
+      ],
+      "metadata": {
+        "id": "Jd73574-rfzi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# wavelength\n",
+        "k_min = c0_min / freq\n",
+        "\n",
+        "# points per wavelength\n",
+        "ppw = k_min / dx\n",
+        "\n",
+        "# number of periods to record\n",
+        "record_periods: int = 3\n",
+        "\n",
+        "# compute points per period\n",
+        "ppp: int = 20\n",
+        "\n",
+        "# CFL number determines time step\n",
+        "cfl = (ppw / ppp)"
+      ],
+      "metadata": {
+        "id": "FZPiOBwMr5QO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "grid_size_points = Vector([Nx, Ny, Nz])\n",
+        "\n",
+        "grid_spacing_meters = Vector([dx, dy, dz])\n",
+        "\n",
+        "# create the k-space grid\n",
+        "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+      ],
+      "metadata": {
+        "id": "DCdgqPsRsCxW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# compute corresponding time stepping\n",
+        "dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+        "\n",
+        "dt_stability_limit = check_stability(kgrid, medium)\n",
+        "\n",
+        "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and\n",
+        "    (dt_stability_limit < dt)):\n",
+        "    dt_old = dt\n",
+        "    ppp = np.ceil( 1.0 / (dt_stability_limit * freq) )\n",
+        "    dt = 1.0 / (ppp * freq)\n",
+        "    if verbose:\n",
+        "        logger.info(\"updated dt\")\n",
+        "else:\n",
+        "    pass\n",
+        "\n",
+        "# calculate the number of time steps to reach steady state\n",
+        "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+        "\n",
+        "# create the time array using an integer number of points per period\n",
+        "Nt = round(t_end / dt)\n",
+        "\n",
+        "# make time array\n",
+        "kgrid.setTime(Nt, dt)\n",
+        "\n",
+        "# calculate the actual CFL after adjusting for dt\n",
+        "cfl_actual = 1.0 / (dt * freq)"
+      ],
+      "metadata": {
+        "id": "PSNLXqyesHlf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# create empty kWaveArray this specfies the transducer properties\n",
+        "karray = kWaveArray(bli_tolerance=0.01,\n",
+        "                    upsampling_rate=16,\n",
+        "                    single_precision=True)\n",
+        "\n",
+        "# set bowl position and orientation\n",
+        "bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),\n",
+        "            kgrid.y_vec[bowl_coords[1]].item(),\n",
+        "            kgrid.z_vec[bowl_coords[2]].item()]\n",
+        "\n",
+        "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+        "             kgrid.y_vec[focus_coords[1]].item(),\n",
+        "             kgrid.z_vec[focus_coords[2]].item()]\n",
+        "\n",
+        "# add bowl shaped element\n",
+        "karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)\n",
+        "\n",
+        "# create time varying source\n",
+        "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+        "                               freq,\n",
+        "                               source_amp,\n",
+        "                               source_phase)\n",
+        "\n",
+        "# make a source object.\n",
+        "source = kSource()\n",
+        "\n",
+        "# assign binary mask using the karray\n",
+        "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+        "\n",
+        "# assign source pressure output in time\n",
+        "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+      ],
+      "metadata": {
+        "id": "ZJZDYFA0sIte"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sensor = kSensor()\n",
+        "\n",
+        "# set sensor mask: the mask says at which points data should be recorded\n",
+        "sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)\n",
+        "\n",
+        "# set the record type: record the pressure waveform\n",
+        "sensor.record = ['p_max', 'p_min']\n",
+        "\n",
+        "# record the final few periods when the field is in steady state\n",
+        "sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1"
+      ],
+      "metadata": {
+        "id": "7Y_mZ3kYsQfI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "DATA_CAST = 'single'\n",
+        "DATA_PATH = './'\n",
+        "\n",
+        "input_filename = tag + '_' + '_' + res + '_input.h5'\n",
+        "output_filename = tag + '_' + '_' + res + '_output.h5'\n",
+        "\n",
+        "# options for writing to file, but not doing simulations\n",
+        "simulation_options = SimulationOptions(\n",
+        "    data_cast=DATA_CAST,\n",
+        "    data_recast=True,\n",
+        "    save_to_disk=True,\n",
+        "    input_filename=input_filename,\n",
+        "    output_filename=output_filename,\n",
+        "    save_to_disk_exit=False,\n",
+        "    data_path=DATA_PATH,\n",
+        "    pml_inside=False)\n",
+        "\n",
+        "execution_options = SimulationExecutionOptions(\n",
+        "    is_gpu_simulation=True,\n",
+        "    delete_data=False,\n",
+        "    verbose_level=2)"
+      ],
+      "metadata": {
+        "id": "UGq4y5L2sVTT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sensor_data = kspaceFirstOrder3DG(\n",
+        "    medium=medium,\n",
+        "    kgrid=kgrid,\n",
+        "    source=source,\n",
+        "    sensor=sensor,\n",
+        "    simulation_options=simulation_options,\n",
+        "    execution_options=execution_options)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VS-cDCOAsuHP",
+        "outputId": "9c4b53ee-c4f5-4522-8042-f2073a3bcd35"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Highest prime factors in each dimension are [211 191  41]\n",
+            "WARNING:root:Use dimension sizes with lower prime factors to improve speed\n",
+            "WARNING:root:DeprecationWarning: Attributes will soon be typed when saved and not saved \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "┌───────────────────────────────────────────────────────────────┐\n",
+            "│                  kspaceFirstOrder-CUDA v1.3                   │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Git hash:            468dc31c2842a7df5f2a07c3a13c16c9b0b2b770 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Reading simulation configuration:                        Done │\n",
+            "│ File format version:                                      1.2 │\n",
+            "│ Selected GPU device id:                                     0 │\n",
+            "│ GPU device name:                                     Tesla T4 │\n",
+            "│ Number of CPU threads:                                      2 │\n",
+            "│ Processor name:                Intel(R) Xeon(R) CPU @ 2.20GHz │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                      Simulation details                       │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Domain dimensions:                            211 x 191 x 246 │\n",
+            "│ Medium type:                                               3D │\n",
+            "│ Simulation time steps:                                   1709 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Input file:  ./bm8__1mm_input.h5                              │\n",
+            "│ Input file:  ./bm8__1mm_output.h5                             │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Compression level:                                          0 │\n",
+            "│ Print progress interval:                                   5% │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Medium details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Wave propagation:                                      Linear │\n",
+            "│ Absorption type:                                    Power law │\n",
+            "│ Medium parameters:                              Heterogeneous │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Source details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure source:                                              │\n",
+            "│ + Source type:                                           Many │\n",
+            "│ + Source condition:                                  Additive │\n",
+            "│ + Memory usage:                                     3121.85MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Sensor details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sensor mask type:                                       Index │\n",
+            "│ Sampling begins at time step:                            1650 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure sensor p_raw                                         │\n",
+            "│ + File usage:                                       1689.47MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Initialization                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Memory allocation:                                       Done │\n",
+            "│ Data loading:                                                 │\n",
+            "│ + Reading input file:                                    Done │\n",
+            "│ + Creating output file:                                  Done │\n",
+            "│ Elapsed time:                                          18.23s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ FFT plans creation:                                      Done │\n",
+            "│ Pre-processing phase:                                    Done │\n",
+            "│ Elapsed time:                                           0.46s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                    Computational resources                    │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Current host memory in use:                            4394MB │\n",
+            "│ Current device memory in use:                          4561MB │\n",
+            "│ Expected output file size:                             1689MB │\n",
+            "│ CUDA solver grid size [blocks x threads]:           320 x 256 │\n",
+            "│ CUDA sampler grid size [blocks x threads]:          320 x 256 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                          Simulation                           │\n",
+            "├──────────┬────────────────┬──────────────┬────────────────────┤\n",
+            "│ Progress │  Elapsed time  │  Time to go  │  Est. finish time  │\n",
+            "├──────────┼────────────────┼──────────────┼────────────────────┤\n",
+            "│     0%   │        0.001s  │      1.176s  │  04/03/24 12:33:18 │\n",
+            "│     5%   │        6.654s  │    124.063s  │  04/03/24 12:35:28 │\n",
+            "│    10%   │       13.900s  │    124.208s  │  04/03/24 12:35:35 │\n",
+            "│    15%   │       21.277s  │    119.660s  │  04/03/24 12:35:38 │\n",
+            "│    20%   │       28.619s  │    113.974s  │  04/03/24 12:35:39 │\n",
+            "│    25%   │       36.098s  │    107.704s  │  04/03/24 12:35:40 │\n",
+            "│    30%   │       43.543s  │    101.233s  │  04/03/24 12:35:42 │\n",
+            "│    35%   │       51.124s  │     94.495s  │  04/03/24 12:35:42 │\n",
+            "│    40%   │       58.665s  │     87.698s  │  04/03/24 12:35:43 │\n",
+            "│    45%   │       66.346s  │     80.717s  │  04/03/24 12:35:44 │\n",
+            "│    50%   │       73.987s  │     73.728s  │  04/03/24 12:35:44 │\n",
+            "│    55%   │       81.675s  │     66.659s  │  04/03/24 12:35:45 │\n",
+            "│    60%   │       89.494s  │     59.430s  │  04/03/24 12:35:46 │\n",
+            "│    65%   │       97.270s  │     52.221s  │  04/03/24 12:35:47 │\n",
+            "│    70%   │      105.181s  │     44.864s  │  04/03/24 12:35:47 │\n",
+            "│    75%   │      113.039s  │     37.533s  │  04/03/24 12:35:47 │\n",
+            "│    80%   │      121.033s  │     30.059s  │  04/03/24 12:35:48 │\n",
+            "│    85%   │      128.982s  │     22.621s  │  04/03/24 12:35:48 │\n",
+            "│    90%   │      137.074s  │     15.043s  │  04/03/24 12:35:49 │\n",
+            "│    95%   │      145.109s  │      7.501s  │  04/03/24 12:35:49 │\n",
+            "├──────────┴────────────────┴──────────────┴────────────────────┤\n",
+            "│ Elapsed time:                                         209.14s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sampled data post-processing:                            Done │\n",
+            "│ Elapsed time:                                           0.00s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                            Summary                            │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Peak host memory in use:                               4450MB │\n",
+            "│ Peak device memory in use:                             4561MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Total execution time:                                 230.96s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                       End of computation                      │\n",
+            "└───────────────────────────────────────────────────────────────┘\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# sampling frequency\n",
+        "fs = 1.0 / kgrid.dt\n",
+        "\n",
+        "# get Fourier coefficients\n",
+        "#amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')\n",
+        "\n",
+        "# reshape data: matlab uses Fortran ordering\n",
+        "p = np.reshape(sensor_data['p_max'].T, (Nx, Ny, Nz), order='F')\n",
+        "\n",
+        "# # get maximum pressure\n",
+        "# pmax = np.nanmax(p)\n",
+        "\n",
+        "# # get location of maximum pressure\n",
+        "# max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='F')\n",
+        "\n",
+        "p_brain = np.empty_like(p)\n",
+        "p_brain.fill(np.nan)\n",
+        "p_brain[brain_mask] = p[brain_mask]\n",
+        "pmax_brain = np.nanmax(p_brain)\n",
+        "max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='F')\n",
+        "\n",
+        "fig3, (ax3a, ax3b) = plt.subplots(1, 2)\n",
+        "im3a = ax3a.pcolormesh(np.squeeze(kgrid.y_vec), np.squeeze(kgrid.z_vec), p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "ax3a.grid(False)\n",
+        "ax3a.axes.get_yaxis().set_visible(False)\n",
+        "ax3a.axes.get_xaxis().set_visible(False)\n",
+        "divider3a = make_axes_locatable(ax3a)\n",
+        "cax3a = divider3a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3a = fig3.colorbar(im3a, cax=cax3a)\n",
+        "cbar_3a.ax.set_title('[MPa]', fontsize='small')\n",
+        "ax3a.invert_yaxis()\n",
+        "im3b = ax3b.pcolormesh(np.squeeze(kgrid.x_vec), np.squeeze(kgrid.z_vec), p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "ax3b.grid(False)\n",
+        "ax3b.axes.get_yaxis().set_visible(False)\n",
+        "ax3b.axes.get_xaxis().set_visible(False)\n",
+        "divider3b = make_axes_locatable(ax3b)\n",
+        "cax3b = divider3b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3b = fig3.colorbar(im3a, cax=cax3b)\n",
+        "cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})\n",
+        "ax3b.invert_yaxis()\n",
+        "plt.tight_layout(pad=1.2)"
+      ],
+      "metadata": {
+        "id": "PgH2bG-oYK_z"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/examples/benchmarks/8/ph1-bm8-sc1.py b/examples/benchmarks/8/ph1-bm8-sc1.py
new file mode 100644
index 000000000..b6f30fbbf
--- /dev/null
+++ b/examples/benchmarks/8/ph1-bm8-sc1.py
@@ -0,0 +1,792 @@
+import numpy as np
+
+import logging
+import sys
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from cycler import cycler
+
+import h5py
+
+from skimage import measure
+from skimage.segmentation import find_boundaries
+from scipy.interpolate import interpn
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+# create logger
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+# create console and file handlers and set level to debug
+ch = logging.StreamHandler(sys.stdout)
+ch.setLevel(logging.DEBUG)
+fh = logging.FileHandler(filename='runner.log')
+fh.setLevel(logging.DEBUG)
+# create formatter
+formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')
+# add formatter to ch, fh
+ch.setFormatter(formatter)
+fh.setFormatter(formatter)
+# add ch, fh to logger
+logger.addHandler(ch)
+logger.addHandler(fh)
+# propagate
+ch.propagate = True
+fh.propagate = True
+logger.propagate = True
+
+verbose: bool = True
+savePlotting: bool = True
+useMaxTimeStep: bool = True
+
+tag = 'bm8'
+res = '1mm'
+transducer = 'sc1'
+
+mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'
+
+if verbose:
+    logger.info(mask_filename)
+
+data = h5py.File(mask_filename, 'r')
+
+if verbose:
+    logger.info( list(data.keys()) )
+
+# is given in millimetres
+dx = data['dx'][:].item()
+
+# scale to metres
+dx = dx / 1000.0
+dy = dx
+dz = dx
+
+xi = np.squeeze(np.asarray(data['xi'][:]))
+yi = np.squeeze(np.asarray(data['yi'][:]))
+zi = np.squeeze(np.asarray(data['zi'][:]))
+
+matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]
+
+skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)
+brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)
+
+skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')
+brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')
+
+water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) +
+                                                     brain_mask.astype(int))
+water_mask = water_mask.astype(bool)
+
+skull_mask = np.swapaxes(skull_mask, 0, 2)
+brain_mask = np.swapaxes(brain_mask, 0, 2)
+water_mask = np.swapaxes(water_mask, 0, 2)
+
+Nx, Ny, Nz = skull_mask.shape
+
+if (transducer == 'sc1'):
+
+    # depth is 64 mm, so is scaled by resolution
+    focus = int(64 / data['dx'][:].item())
+
+    focus_coords = [(Nx-1) // 2, (Ny-1) // 2, focus]
+
+    bowl_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]
+
+if (transducer == 'sc2'):
+    focus_coords = [(Nx-1)//2, (Ny-1)//2, (Nz-1)//2]
+    disc_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones(skull_mask.shape)
+density = 1000.0 * np.ones(skull_mask.shape)
+alpha_coeff = np.zeros(skull_mask.shape)
+
+# non-dispersive
+alpha_power = 2.0
+
+# skull
+sound_speed[skull_mask] = 2800.0
+density[skull_mask] = 1850.0
+alpha_coeff[skull_mask] = 4.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(sound_speed.flatten())
+c0_max = np.min(sound_speed.flatten())
+
+medium = kWaveMedium(
+    sound_speed=sound_speed,
+    density=density,
+    alpha_coeff=alpha_coeff,
+    alpha_power=alpha_power
+)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# single spherical transducer
+if (transducer == 'sc1'):
+
+    # bowl radius of curvature [m]
+    source_roc = 64.0e-3
+
+    # as we will use the bowl element this has to be a int or float
+    diameters = 64.0e-3
+
+elif (transducer == 'sc2'):
+
+    # diameter of the disc
+    diameter = 20e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min = c0_min / freq
+
+# points per wavelength
+ppw = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 20
+
+# CFL number determines time step
+cfl = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Ny, Nz])
+
+grid_spacing_meters = Vector([dx, dy, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+msg = "dt_stability_limit=" + str(dt_stability_limit) + ", dt=" + str(dt)
+if verbose:
+    logger.info(msg)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and
+    (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil( 1.0 / (dt_stability_limit * freq) )
+    dt = 1.0 / (ppp * freq)
+    if verbose:
+        logger.info("updated dt")
+else:
+    if verbose:
+        logger.info("not updated dt")
+
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+if verbose:
+    logger.info('PPW = ' + str(ppw))
+    logger.info('CFL = ' + str(cfl_actual))
+    logger.info('PPP = ' + str(ppp))
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+if verbose:
+    logger.info("kSource")
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+if (transducer == 'sc1'):
+
+    # set bowl position and orientation
+    bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+                kgrid.y_vec[bowl_coords[1]].item(),
+                kgrid.z_vec[bowl_coords[2]].item()]
+
+    focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+                 kgrid.y_vec[focus_coords[1]].item(),
+                 kgrid.z_vec[focus_coords[2]].item()]
+
+    # add bowl shaped element
+    karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)
+
+elif (transducer == 'sc2'):
+
+    # set disc position
+    position = [kgrid.x_vec[disc_coords[2]].item(),
+                kgrid.y_vec[disc_coords[2]].item(),
+                kgrid.z_vec[disc_coords[2]].item()]
+
+    # arbitrary position
+    focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+                 kgrid.y_vec[focus_coords[1]].item(),
+                 kgrid.z_vec[focus_coords[2]].item()]
+
+    # add disc-shaped planar element
+    karray.add_disc_element(position, diameter, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+if verbose:
+    logger.info("kSensor")
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = tag + '_' + transducer + '_' + res + '_input.h5'
+output_filename = tag + '_' + transducer + '_' + res + '_output.h5'
+
+# set input options
+if verbose:
+    logger.info("simulation_options")
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+if verbose:
+    logger.info("execution_options")
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+if verbose:
+    logger.info("kspaceFirstOrder3DG")
+
+sensor_data = kspaceFirstOrder3DG(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# POST-PROCESS
+# =========================================================================
+
+if verbose:
+    logger.info("post processing")
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+if verbose:
+    logger.info("extract_amp_phase")
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1,
+                              fft_padding=1, window='Rectangular')
+
+# reshape data: matlab uses Fortran ordering
+p = np.reshape(amp, (Nx, Ny, Nz), order='F')
+pmax = np.nanmax(p)
+max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='C')
+
+p_water = np.empty_like(p)
+p_water.fill(np.nan)
+p_water[water_mask] = p[water_mask]
+pmax_water = np.nanmax(p_water)
+max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='C')
+
+p_skull = np.empty_like(p)
+p_skull.fill(np.nan)
+p_skull[skull_mask] = p[skull_mask]
+pmax_skull = np.nanmax(p_skull)
+max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='C')
+
+p_brain = np.empty_like(p)
+p_brain.fill(np.nan)
+p_brain[brain_mask] = p[brain_mask]
+pmax_brain = np.nanmax(p_brain)
+max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')
+
+# domain axes
+x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)
+y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)
+z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+
+# colours
+cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
+
+# brain axes
+# x
+x_x = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_x = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]
+coefficients_x = np.polyfit(x_x, y_x, 1)
+polynomial_x = np.poly1d(coefficients_x)
+axis = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+beam_axis_x = polynomial_x(z_vec)
+# y
+x_y = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_y = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]
+coefficients_y = np.polyfit(x_y, y_y, 1)
+polynomial_y = np.poly1d(coefficients_y)
+beam_axis_y = polynomial_y(z_vec)
+# beam axis
+beam_axis = np.vstack((beam_axis_x, beam_axis_y, z_vec)).T
+# interpolate for pressure on brain axis
+beam_pressure_brain = interpn((x_vec, y_vec, z_vec) , p, beam_axis,
+                    method='linear', bounds_error=False, fill_value=np.nan)
+
+# skull axes
+# x
+x_x = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_x = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]
+coefficients_x = np.polyfit(x_x, y_x, 1)
+polynomial_x = np.poly1d(coefficients_x)
+axis = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+beam_axis_x = polynomial_x(z_vec)
+# y
+x_y = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_y = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]
+coefficients_y = np.polyfit(x_y, y_y, 1)
+polynomial_y = np.poly1d(coefficients_y)
+beam_axis_y = polynomial_y(z_vec)
+
+# beam axis
+beam_axis = np.vstack((beam_axis_x, beam_axis_y, z_vec)).T
+
+# interpolate for pressure
+beam_pressure_skull = interpn((x_vec, y_vec, z_vec) , p, beam_axis,
+                    method='linear', bounds_error=False, fill_value=np.nan)
+
+
+
+# plot pressure on through centre lines
+fig1, ax1 = plt.subplots()
+ax1.plot(p[(Nx-1)//2, (Nx-1)//2, :] / 1e6, label='geometric')
+ax1.plot(beam_pressure_brain / 1e6, label='focal')
+ax1.plot(beam_pressure_skull / 1e6, label='skull')
+ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)
+ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)
+ax1.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [MPa]',
+        title='Centreline Pressures')
+ax1.legend()
+ax1.grid(True)
+
+
+
+def get_edges(mask, fill_with_nan=True):
+    """returns the mask as a float array and Np.NaN"""
+    edges = find_boundaries(mask, mode='thin').astype(np.float32)
+    if fill_with_nan:
+        edges[edges == 0] = np.nan
+    return edges
+
+# contouring block
+
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int), fill_with_nan=False)
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int), fill_with_nan=False)
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int), fill_with_nan=False)
+
+contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)
+contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)
+contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)
+
+if verbose:
+    msg = "size of contours:" + str(np.shape(contour_x)) + ", " + str(np.shape(contour_y)) + ", " + str(np.shape(contour_z)) + "."
+    logger.info(msg)
+    msg = "number of contours: (" + str(num_x) + ", " + str(num_y) + ", " + str(num_z) + ")."
+    logger.info(msg)
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_x):
+    j = np.argmax(np.where(contour_x[:, Nz //2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_x[:, Nz //2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+contours_x_inner = measure.find_contours(np.where(contour_x==i_inner, 1, 0))
+if not contours_x_inner:
+    logger.warning("size of contours_x_inner is zero")
+contours_x_outer = measure.find_contours(np.where(contour_x==i_outer, 1, 0))
+if not contours_x_outer:
+    logger.warning("size of contours_x_outer is zero")
+inner_index_x = float(Ny)
+outer_index_x = float(0)
+for i in range( len(contours_x_inner) ):
+    x_min = np.min(contours_x_inner[i][:, 1])
+    if (x_min < inner_index_x):
+        inner_index_x = i
+for i in range( len(contours_x_outer) ):
+    x_max = np.max(contours_x_outer[i][:, 1])
+    if (x_max > outer_index_x):
+        outer_index_x = i
+
+jmax = 0
+jmin = Nx
+i_inner = None
+i_outer = None
+for i in range(num_y):
+    j = np.argmax(np.where(contour_y[:, Nz // 2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_y[:, Nz // 2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+contours_y_inner = measure.find_contours(np.where(contour_y==i_inner, 1, 0))
+if not contours_y_inner:
+    logger.warning("size of contours_y_inner is zero")
+contours_y_outer = measure.find_contours(np.where(contour_y==i_outer, 1, 0))
+if not contours_y_outer:
+    logger.warning("size of contours_y_outer is zero")
+inner_index_y = float(Nx)
+outer_index_y = float(0)
+for i in range( len(contours_y_inner) ):
+    y_min = np.min(contours_y_inner[i][:, 1])
+    if (y_min < inner_index_y):
+        inner_index_y = i
+for i in range( len(contours_y_outer) ):
+    y_max = np.max(contours_y_outer[i][:, 1])
+    if (y_max > outer_index_y):
+        outer_index_y = i
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_z):
+    j = np.argmax(np.where(contour_z[:, Nx // 2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_z[:, Nx // 2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+
+contours_z_inner = measure.find_contours(np.where(contour_z==i_inner, 1, 0))
+if not contours_z_inner:
+    logger.warning("size of contours_z_inner is zero")
+else:
+    inner_index_z = float(Nx)
+    for i in range( len(contours_z_inner) ):
+        z_min = np.min(contours_z_inner[i][:, 1])
+        if (z_min < inner_index_z):
+            inner_index_z = i
+
+contours_z_outer = measure.find_contours(np.where(contour_z==i_outer, 1, 0))
+if not contours_z_outer:
+    logger.warning("size of contours_z_outer is zero")
+else:
+    outer_index_z = float(0)
+    for i in range( len(contours_z_outer) ):
+        z_max = np.max(contours_z_outer[i][:, 1])
+        if (z_max > outer_index_z):
+            outer_index_z = i
+
+# end of contouring block
+
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int), fill_with_nan=True)
+
+# plot the pressure field at mid point along z axis
+fig2, ax2 = plt.subplots()
+im2 = ax2.imshow(p[:, :, max_loc_brain[2]] / 1e6,
+            aspect='auto',
+            interpolation='none',
+            origin='lower',
+            cmap='viridis')
+
+if not contours_z_inner:
+    ax2.imshow(edges_z, aspect='auto', interpolation='none',
+               cmap='Greys', origin='upper')
+else:
+      ax2.plot(contours_z_inner[inner_index_z][:, 1],
+               contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax2.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+
+ax2.set(xlabel=r'$x$ [mm]',
+        ylabel=r'$y$ [mm]',
+        title='Pressure Field')
+ax2.grid(False)
+divider2 = make_axes_locatable(ax2)
+cax2 = divider2.append_axes("right", size="5%", pad=0.05)
+cbar_2 = fig2.colorbar(im2, cax=cax2)
+cbar_2.ax.set_title('[MPa]', fontsize='small')
+
+pwater_max_x = np.nanmax(p_water[max_loc_brain[0], :, :].flatten())
+pskull_max_x = np.nanmax(p_skull[max_loc_brain[0], :, :].flatten())
+pbrain_max_x = np.nanmax(p_brain[max_loc_brain[0], :, :].flatten())
+
+pwater_max_y = np.nanmax(p_water[:, max_loc_brain[1], :].flatten())
+pskull_max_y = np.nanmax(p_skull[:, max_loc_brain[1], :].flatten())
+pbrain_max_y = np.nanmax(p_brain[:, max_loc_brain[1], :].flatten())
+
+fig3, (ax3a, ax3b) = plt.subplots(1,2)
+im3a_water = ax3a.imshow(p_water[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='cool')
+im3a_skull = ax3a.imshow(p_skull[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='turbo')
+im3a_brain = ax3a.imshow(p_brain[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='viridis')
+
+ax3a.plot(contours_x_inner[inner_index_x][:, 1],
+          contours_x_inner[inner_index_x][:, 0], 'k', linewidth=0.5)
+ax3a.plot(contours_x_outer[outer_index_x][:, 1],
+          contours_x_outer[outer_index_x][:, 0], 'k', linewidth=0.5)
+
+ax3a.grid(False)
+ax3a.axes.get_yaxis().set_visible(False)
+ax3a.axes.get_xaxis().set_visible(False)
+divider3a = make_axes_locatable(ax3a)
+cax3a = divider3a.append_axes("right", size="5%", pad=0.05)
+cbar_3a = fig3.colorbar(im3a_brain, cax=cax3a)
+cbar_3a.ax.set_title('[kPa]', fontsize='small')
+ax3b.imshow(p_water[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='cool')
+ax3b.imshow(p_skull[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='turbo')
+im3b_brain = ax3b.imshow(p_brain[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='viridis')
+
+ax3b.grid(False)
+ax3b.axes.get_yaxis().set_visible(False)
+ax3b.axes.get_xaxis().set_visible(False)
+divider3b = make_axes_locatable(ax3b)
+cax3b = divider3b.append_axes("right", size="5%", pad=0.05)
+cbar_3b = fig3.colorbar(im3b_brain, cax=cax3b)
+cbar_3b.ax.set_title('[Pa]', fontdict={'fontsize':8})
+
+
+fig4, ax4 = plt.subplots()
+if not contours_z_inner:
+    pass
+else:
+    ax4.plot(contours_z_inner[inner_index_z][:, 1],
+             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax4.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+
+
+fig5, (ax5a, ax5b) = plt.subplots(1,2)
+im5a = ax5a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5a_boundary = ax5a.imshow(edges_x, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5a.grid(False)
+ax5a.axes.get_yaxis().set_visible(False)
+ax5a.axes.get_xaxis().set_visible(False)
+divider5a = make_axes_locatable(ax5a)
+cax5a = divider5a.append_axes("right", size="5%", pad=0.05)
+cbar_5a = fig5.colorbar(im5a, cax=cax5a)
+cbar_5a.ax.set_title('[MPa]', fontsize='small')
+im5b = ax5b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5b_boundary = ax5b.imshow(edges_y, aspect='auto', interpolation='none',
+                            cmap='Greys',origin='upper', alpha=0.75)
+ax5b.grid(False)
+ax5b.axes.get_yaxis().set_visible(False)
+ax5b.axes.get_xaxis().set_visible(False)
+divider5b = make_axes_locatable(ax5b)
+cax5b = divider5b.append_axes("right", size="5%", pad=0.05)
+cbar_5b = fig5.colorbar(im5b, cax=cax5b)
+cbar_5b.ax.set_title('[MPa]', fontsize='small')
+    
+all_contours_x = []
+for i in range(num_x):
+    all_contours_x.append(measure.find_contours(np.where(contour_x==(i+1), 1, 0)))
+
+all_contours_y = []
+for i in range(num_y):
+    all_contours_y.append(measure.find_contours(np.where(contour_y==(i+1), 1, 0)))
+
+fig6, (ax6a, ax6b) = plt.subplots(1,2)
+im6a = ax6a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+for contour in all_contours_x:
+    # logger.info(contour  dir(contour))
+    for i in range( len(contour) ):
+        ax6a.plot(contour[i][:, 1], contour[i][:, 0], 'w', linewidth=0.5)
+
+ax6a.grid(False)
+ax6a.axes.get_yaxis().set_visible(False)
+ax6a.axes.get_xaxis().set_visible(False)
+divider6a = make_axes_locatable(ax5a)
+cax6a = divider6a.append_axes("right", size="5%", pad=0.05)
+cbar_6a = fig6.colorbar(im6a, cax=cax6a)
+cbar_6a.ax.set_title('[MPa]', fontsize='small')
+im6b = ax6b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+
+custom_cycler = cycler(ls=['-', '--', ':', '-.'])
+
+ax6b.set_prop_cycle(custom_cycler)
+
+for idx, contour in enumerate(all_contours_y):
+    for i in range( len(contour) ):
+        ax6b.plot(contour[i][:, 1], contour[i][:, 0], c=cycle[idx],
+                  linewidth=0.5, label=str(idx))
+ax6b.legend()
+ax6b.grid(False)
+ax6b.axes.get_yaxis().set_visible(False)
+ax6b.axes.get_xaxis().set_visible(False)
+divider6b = make_axes_locatable(ax6b)
+cax6b = divider6b.append_axes("right", size="5%", pad=0.05)
+cbar_6b = fig6.colorbar(im6b, cax=cax6b)
+cbar_6b.ax.set_title('[MPa]', fontsize='small')
+
+
diff --git a/examples/benchmarks/8/ph1-bm8-sc2.py b/examples/benchmarks/8/ph1-bm8-sc2.py
new file mode 100644
index 000000000..9973f286f
--- /dev/null
+++ b/examples/benchmarks/8/ph1-bm8-sc2.py
@@ -0,0 +1,743 @@
+import numpy as np
+
+import logging
+import sys
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from cycler import cycler
+
+import h5py
+
+from skimage import measure
+from skimage.segmentation import find_boundaries
+from scipy.interpolate import interpn
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+# create logger
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+# create console and file handlers and set level to debug
+ch = logging.StreamHandler(sys.stdout)
+ch.setLevel(logging.DEBUG)
+fh = logging.FileHandler(filename='runner.log')
+fh.setLevel(logging.DEBUG)
+# create formatter
+formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')
+# add formatter to ch, fh
+ch.setFormatter(formatter)
+fh.setFormatter(formatter)
+# add ch, fh to logger
+logger.addHandler(ch)
+logger.addHandler(fh)
+# propagate
+ch.propagate = True
+fh.propagate = True
+logger.propagate = True
+
+verbose: bool = True
+savePlotting: bool = True
+useMaxTimeStep: bool = True
+
+tag = 'bm8'
+res = '1mm'
+transducer = 'sc2'
+
+mask_filename = 'skull_mask_' + tag + '_dx_' + res + '.mat'
+
+data = h5py.File(mask_filename, 'r')
+
+# is given in millimetres
+dx = data['dx'][:].item()
+
+# scale to metres
+dx = dx / 1000.0
+dy = dx
+dz = dx
+
+xi = np.squeeze(np.asarray(data['xi'][:]))
+yi = np.squeeze(np.asarray(data['yi'][:]))
+zi = np.squeeze(np.asarray(data['zi'][:]))
+
+matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]
+
+skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)
+brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)
+
+skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')
+brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')
+
+water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) +
+                                                     brain_mask.astype(int))
+water_mask = water_mask.astype(bool)
+
+skull_mask = np.swapaxes(skull_mask, 0, 2)
+brain_mask = np.swapaxes(brain_mask, 0, 2)
+water_mask = np.swapaxes(water_mask, 0, 2)
+
+Nx, Ny, Nz = skull_mask.shape
+
+if (transducer == 'sc1'):
+
+    # depth is 64 mm, so is scaled by resolution
+    focus = int(64 / data['dx'][:].item())
+
+    focus_coords = [(Nx-1) // 2, (Ny-1) // 2, focus]
+
+    bowl_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]
+
+if (transducer == 'sc2'):
+    focus_coords = [(Nx-1) // 2, (Ny-1) // 2, (Nz-1) // 2]
+    disc_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]
+
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones(skull_mask.shape)
+density = 1000.0 * np.ones(skull_mask.shape)
+alpha_coeff = np.zeros(skull_mask.shape)
+
+# non-dispersive
+alpha_power = 2.0
+
+# skull
+sound_speed[skull_mask] = 2800.0
+density[skull_mask] = 1850.0
+alpha_coeff[skull_mask] = 4.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(sound_speed.flatten())
+c0_max = np.min(sound_speed.flatten())
+
+medium = kWaveMedium(
+    sound_speed=sound_speed,
+    density=density,
+    alpha_coeff=alpha_coeff,
+    alpha_power=alpha_power
+)
+
+# =========================================================================
+# DEFINE THE TRANSDUCER SETUP
+# =========================================================================
+
+# single spherical transducer
+if (transducer == 'sc1'):
+
+    # bowl radius of curvature [m]
+    source_roc = 64.0e-3
+
+    # as we will use the bowl element this has to be a int or float
+    diameters = 64.0e-3
+
+elif (transducer == 'sc2'):
+
+    # diameter of the disc
+    diameter = 20e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+
+# =========================================================================
+# DEFINE COMPUTATIONAL PARAMETERS
+# =========================================================================
+
+# wavelength
+k_min = c0_min / freq
+
+# points per wavelength
+ppw = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 20
+
+# CFL number determines time step
+cfl = (ppw / ppp)
+
+
+# =========================================================================
+# DEFINE THE KGRID
+# =========================================================================
+
+grid_size_points = Vector([Nx, Ny, Nz])
+
+grid_spacing_meters = Vector([dx, dy, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+
+# =========================================================================
+# DEFINE THE TIME VECTOR
+# =========================================================================
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and
+    (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil( 1.0 / (dt_stability_limit * freq) )
+    dt = 1.0 / (ppp * freq)
+    if verbose:
+        print("updated dt")
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+
+# =========================================================================
+# DEFINE THE SOURCE PARAMETERS
+# =========================================================================
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+if (transducer == 'sc1'):
+
+    # set bowl position and orientation
+    bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+                kgrid.y_vec[bowl_coords[1]].item(),
+                kgrid.z_vec[bowl_coords[2]].item()]
+
+    focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+                 kgrid.y_vec[focus_coords[1]].item(),
+                 kgrid.z_vec[focus_coords[2]].item()]
+
+    # add bowl shaped element
+    karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)
+
+elif (transducer == 'sc2'):
+
+    # set disc position
+    position = [kgrid.x_vec[disc_coords[2]].item(),
+                kgrid.y_vec[disc_coords[2]].item(),
+                kgrid.z_vec[disc_coords[2]].item()]
+
+    # arbitrary position
+    focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+                 kgrid.y_vec[focus_coords[1]].item(),
+                 kgrid.z_vec[focus_coords[2]].item()]
+
+    # add disc-shaped planar element
+    karray.add_disc_element(position, diameter, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+
+# =========================================================================
+# DEFINE THE SENSOR PARAMETERS
+# =========================================================================
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1
+
+
+# =========================================================================
+# DEFINE THE SIMULATION PARAMETERS
+# =========================================================================
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = tag + '_' + transducer + '_' + res + '_input.h5'
+output_filename = tag + '_' + transducer + '_' + res + '_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+
+# =========================================================================
+# RUN THE SIMULATION
+# =========================================================================
+
+sensor_data = kspaceFirstOrder3DG(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+
+# =========================================================================
+# POST-PROCESS
+# =========================================================================
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1,
+                              fft_padding=1, window='Rectangular')
+
+# reshape data: matlab uses Fortran ordering
+p = np.reshape(amp, (Nx, Ny, Nz), order='F')
+pmax = np.nanmax(p)
+max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='C')
+
+p_water = np.empty_like(p)
+p_water.fill(np.nan)
+p_water[water_mask] = p[water_mask]
+pmax_water = np.nanmax(p_water)
+max_loc_water = np.unravel_index(np.nanargmax(p_water), p.shape, order='C')
+
+p_skull = np.empty_like(p)
+p_skull.fill(np.nan)
+p_skull[skull_mask] = p[skull_mask]
+pmax_skull = np.nanmax(p_skull)
+max_loc_skull = np.unravel_index(np.nanargmax(p_skull), p.shape, order='C')
+
+p_brain = np.empty_like(p)
+p_brain.fill(np.nan)
+p_brain[brain_mask] = p[brain_mask]
+pmax_brain = np.nanmax(p_brain)
+max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='C')
+
+# domain axes
+x_vec = np.linspace(kgrid.x_vec[0].item(), kgrid.x_vec[-1].item(), kgrid.Nx)
+y_vec = np.linspace(kgrid.y_vec[0].item(), kgrid.y_vec[-1].item(), kgrid.Ny)
+z_vec = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+
+# colours
+cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
+
+# brain axes
+# x
+x_x = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_x = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_brain[0]].item()]
+coefficients_x = np.polyfit(x_x, y_x, 1)
+polynomial_x = np.poly1d(coefficients_x)
+axis = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+beam_axis_x = polynomial_x(z_vec)
+# y
+x_y = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_brain[2]].item()]
+y_y = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_brain[1]].item()]
+coefficients_y = np.polyfit(x_y, y_y, 1)
+polynomial_y = np.poly1d(coefficients_y)
+beam_axis_y = polynomial_y(z_vec)
+# beam axis
+beam_axis = np.vstack((beam_axis_x, beam_axis_y, z_vec)).T
+# interpolate for pressure on brain axis
+beam_pressure_brain = interpn((x_vec, y_vec, z_vec) , p, beam_axis,
+                    method='linear', bounds_error=False, fill_value=np.nan)
+
+# skull axes
+# x
+x_x = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_x = [kgrid.x_vec[bowl_coords[0]].item(), kgrid.x_vec[max_loc_skull[0]].item()]
+coefficients_x = np.polyfit(x_x, y_x, 1)
+polynomial_x = np.poly1d(coefficients_x)
+axis = np.linspace(kgrid.z_vec[0].item(), kgrid.z_vec[-1].item(), kgrid.Nz)
+beam_axis_x = polynomial_x(z_vec)
+# y
+x_y = [kgrid.z_vec[bowl_coords[2]].item(), kgrid.z_vec[max_loc_skull[2]].item()]
+y_y = [kgrid.y_vec[bowl_coords[1]].item(), kgrid.y_vec[max_loc_skull[1]].item()]
+coefficients_y = np.polyfit(x_y, y_y, 1)
+polynomial_y = np.poly1d(coefficients_y)
+beam_axis_y = polynomial_y(z_vec)
+
+# beam axis
+beam_axis = np.vstack((beam_axis_x, beam_axis_y, z_vec)).T
+
+# interpolate for pressure
+beam_pressure_skull = interpn((x_vec, y_vec, z_vec) , p, beam_axis,
+                    method='linear', bounds_error=False, fill_value=np.nan)
+
+# plot pressure on through centre lines
+fig1, ax1 = plt.subplots()
+ax1.plot(p[(Nx-1)//2, (Nx-1)//2, :] / 1e6, label='geometric')
+ax1.plot(beam_pressure_brain / 1e6, label='focal')
+ax1.plot(beam_pressure_skull / 1e6, label='skull')
+ax1.hlines(pmax_brain / 1e6, 0, len(z_vec), color=cycle[1], linestyle='dashed', lw=0.5)
+ax1.hlines(pmax_skull / 1e6, 0, len(z_vec), color=cycle[2], linestyle='dashed', lw=0.5)
+ax1.set(xlabel='Axial Position [mm]',
+        ylabel='Pressure [MPa]',
+        title='Centreline Pressures')
+ax1.legend()
+ax1.grid(True)
+
+
+
+def get_edges(mask, fill_with_nan=True):
+    """returns the mask as a float array and Np.NaN"""
+    edges = find_boundaries(mask, mode='thin').astype(np.float32)
+    if fill_with_nan:
+        edges[edges == 0] = np.nan
+    return edges
+
+# contouring block
+
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int), fill_with_nan=False)
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int), fill_with_nan=False)
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int), fill_with_nan=False)
+
+contour_x, num_x = measure.label(edges_x, background=0, return_num=True, connectivity=2)
+contour_y, num_y = measure.label(edges_y, background=0, return_num=True, connectivity=2)
+contour_z, num_z = measure.label(edges_z, background=0, return_num=True, connectivity=2)
+
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_x):
+    j = np.argmax(np.where(contour_x[:, Nz //2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_x[:, Nz //2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+contours_x_inner = measure.find_contours(np.where(contour_x==i_inner, 1, 0))
+if not contours_x_inner:
+    logger.warning("size of contours_x_inner is zero")
+contours_x_outer = measure.find_contours(np.where(contour_x==i_outer, 1, 0))
+if not contours_x_outer:
+    logger.warning("size of contours_x_outer is zero")
+inner_index_x = float(Ny)
+outer_index_x = float(0)
+for i in range( len(contours_x_inner) ):
+    x_min = np.min(contours_x_inner[i][:, 1])
+    if (x_min < inner_index_x):
+        inner_index_x = i
+for i in range( len(contours_x_outer) ):
+    x_max = np.max(contours_x_outer[i][:, 1])
+    if (x_max > outer_index_x):
+        outer_index_x = i
+
+jmax = 0
+jmin = Nx
+i_inner = None
+i_outer = None
+for i in range(num_y):
+    j = np.argmax(np.where(contour_y[:, Nz // 2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_y[:, Nz // 2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+contours_y_inner = measure.find_contours(np.where(contour_y==i_inner, 1, 0))
+if not contours_y_inner:
+    logger.warning("size of contours_y_inner is zero")
+contours_y_outer = measure.find_contours(np.where(contour_y==i_outer, 1, 0))
+if not contours_y_outer:
+    logger.warning("size of contours_y_outer is zero")
+inner_index_y = float(Nx)
+outer_index_y = float(0)
+for i in range( len(contours_y_inner) ):
+    y_min = np.min(contours_y_inner[i][:, 1])
+    if (y_min < inner_index_y):
+        inner_index_y = i
+for i in range( len(contours_y_outer) ):
+    y_max = np.max(contours_y_outer[i][:, 1])
+    if (y_max > outer_index_y):
+        outer_index_y = i
+
+jmax = 0
+jmin = Ny
+i_inner = None
+i_outer = None
+for i in range(num_z):
+    j = np.argmax(np.where(contour_z[:, Nx // 2]==(i+1), 1, 0))
+    if (j > jmax):
+        jmax = j
+        i_outer = i+1
+    k = np.argmin(np.where(contour_z[:, Nx // 2]==(i+1), 0, 1))
+    if (k < jmin):
+        jmin = k
+        i_inner = i+1
+
+contours_z_inner = measure.find_contours(np.where(contour_z==i_inner, 1, 0))
+if not contours_z_inner:
+    logger.warning("size of contours_z_inner is zero")
+else:
+    inner_index_z = float(Nx)
+    for i in range( len(contours_z_inner) ):
+        z_min = np.min(contours_z_inner[i][:, 1])
+        if (z_min < inner_index_z):
+            inner_index_z = i
+
+contours_z_outer = measure.find_contours(np.where(contour_z==i_outer, 1, 0))
+if not contours_z_outer:
+    logger.warning("size of contours_z_outer is zero")
+else:
+    outer_index_z = float(0)
+    for i in range( len(contours_z_outer) ):
+        z_max = np.max(contours_z_outer[i][:, 1])
+        if (z_max > outer_index_z):
+            outer_index_z = i
+
+# end of contouring block
+
+edges_x = get_edges(np.transpose(skull_mask[max_loc_brain[0], :, :]).astype(int))
+edges_y = get_edges(np.transpose(skull_mask[:, max_loc_brain[1], :]).astype(int))
+edges_z = get_edges(np.transpose(skull_mask[:, :, max_loc_brain[2]]).astype(int), fill_with_nan=True)
+
+# plot the pressure field at mid point along z axis
+fig2, ax2 = plt.subplots()
+im2 = ax2.imshow(p[:, :, max_loc_brain[2]] / 1e6,
+            aspect='auto',
+            interpolation='none',
+            origin='lower',
+            cmap='viridis')
+
+if not contours_z_inner:
+    ax2.imshow(edges_z, aspect='auto', interpolation='none',
+               cmap='Greys', origin='upper')
+else:
+      ax2.plot(contours_z_inner[inner_index_z][:, 1],
+               contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax2.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+
+ax2.set(xlabel=r'$x$ [mm]',
+        ylabel=r'$y$ [mm]',
+        title='Pressure Field')
+ax2.grid(False)
+divider2 = make_axes_locatable(ax2)
+cax2 = divider2.append_axes("right", size="5%", pad=0.05)
+cbar_2 = fig2.colorbar(im2, cax=cax2)
+cbar_2.ax.set_title('[MPa]', fontsize='small')
+
+pwater_max_x = np.nanmax(p_water[max_loc_brain[0], :, :].flatten())
+pskull_max_x = np.nanmax(p_skull[max_loc_brain[0], :, :].flatten())
+pbrain_max_x = np.nanmax(p_brain[max_loc_brain[0], :, :].flatten())
+
+pwater_max_y = np.nanmax(p_water[:, max_loc_brain[1], :].flatten())
+pskull_max_y = np.nanmax(p_skull[:, max_loc_brain[1], :].flatten())
+pbrain_max_y = np.nanmax(p_brain[:, max_loc_brain[1], :].flatten())
+
+fig3, (ax3a, ax3b) = plt.subplots(1,2)
+im3a_water = ax3a.imshow(p_water[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='cool')
+im3a_skull = ax3a.imshow(p_skull[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='turbo')
+im3a_brain = ax3a.imshow(p_brain[max_loc_brain[0], :, :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='viridis')
+
+ax3a.plot(contours_x_inner[inner_index_x][:, 1],
+          contours_x_inner[inner_index_x][:, 0], 'k', linewidth=0.5)
+ax3a.plot(contours_x_outer[outer_index_x][:, 1],
+          contours_x_outer[outer_index_x][:, 0], 'k', linewidth=0.5)
+
+ax3a.grid(False)
+ax3a.axes.get_yaxis().set_visible(False)
+ax3a.axes.get_xaxis().set_visible(False)
+divider3a = make_axes_locatable(ax3a)
+cax3a = divider3a.append_axes("right", size="5%", pad=0.05)
+cbar_3a = fig3.colorbar(im3a_brain, cax=cax3a)
+cbar_3a.ax.set_title('[kPa]', fontsize='small')
+ax3b.imshow(p_water[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='cool')
+ax3b.imshow(p_skull[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='turbo')
+im3b_brain = ax3b.imshow(p_brain[:, max_loc_brain[1], :].T,
+            aspect='auto',
+            interpolation='none',
+            origin='upper',
+            cmap='viridis')
+
+ax3b.grid(False)
+ax3b.axes.get_yaxis().set_visible(False)
+ax3b.axes.get_xaxis().set_visible(False)
+divider3b = make_axes_locatable(ax3b)
+cax3b = divider3b.append_axes("right", size="5%", pad=0.05)
+cbar_3b = fig3.colorbar(im3b_brain, cax=cax3b)
+cbar_3b.ax.set_title('[Pa]', fontdict={'fontsize':8})
+
+
+fig4, ax4 = plt.subplots()
+if not contours_z_inner:
+    pass
+else:
+    ax4.plot(contours_z_inner[inner_index_z][:, 1],
+             contours_z_inner[inner_index_z][:, 0], 'w', linewidth=0.5)
+if not contours_z_outer:
+    pass
+else:
+    ax4.plot(contours_z_outer[outer_index_z][:, 1],
+             contours_z_outer[outer_index_z][:, 0], 'w', linewidth=0.5)
+
+
+fig5, (ax5a, ax5b) = plt.subplots(1,2)
+im5a = ax5a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5a_boundary = ax5a.imshow(edges_x, aspect='auto', interpolation='none',
+                            cmap='Greys', origin='upper', alpha=0.75)
+ax5a.grid(False)
+ax5a.axes.get_yaxis().set_visible(False)
+ax5a.axes.get_xaxis().set_visible(False)
+divider5a = make_axes_locatable(ax5a)
+cax5a = divider5a.append_axes("right", size="5%", pad=0.05)
+cbar_5a = fig5.colorbar(im5a, cax=cax5a)
+cbar_5a.ax.set_title('[MPa]', fontsize='small')
+im5b = ax5b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+im5b_boundary = ax5b.imshow(edges_y, aspect='auto', interpolation='none',
+                            cmap='Greys',origin='upper', alpha=0.75)
+ax5b.grid(False)
+ax5b.axes.get_yaxis().set_visible(False)
+ax5b.axes.get_xaxis().set_visible(False)
+divider5b = make_axes_locatable(ax5b)
+cax5b = divider5b.append_axes("right", size="5%", pad=0.05)
+cbar_5b = fig5.colorbar(im5b, cax=cax5b)
+cbar_5b.ax.set_title('[MPa]', fontsize='small')
+    
+all_contours_x = []
+for i in range(num_x):
+    all_contours_x.append(measure.find_contours(np.where(contour_x==(i+1), 1, 0)))
+
+all_contours_y = []
+for i in range(num_y):
+    all_contours_y.append(measure.find_contours(np.where(contour_y==(i+1), 1, 0)))
+
+fig6, (ax6a, ax6b) = plt.subplots(1,2)
+im6a = ax6a.imshow(p[max_loc_brain[0], :, :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+for contour in all_contours_x:
+    for i in range( len(contour) ):
+        ax6a.plot(contour[i][:, 1], contour[i][:, 0], 'w', linewidth=0.5)
+
+ax6a.grid(False)
+ax6a.axes.get_yaxis().set_visible(False)
+ax6a.axes.get_xaxis().set_visible(False)
+divider6a = make_axes_locatable(ax5a)
+cax6a = divider6a.append_axes("right", size="5%", pad=0.05)
+cbar_6a = fig6.colorbar(im6a, cax=cax6a)
+cbar_6a.ax.set_title('[MPa]', fontsize='small')
+im6b = ax6b.imshow(p[:, max_loc_brain[1], :].T / 1e6,
+                   vmin=0, vmax=pmax / 1e6,
+                   aspect='auto',
+                   interpolation='none',
+                   origin='upper',
+                   cmap='viridis')
+
+custom_cycler = cycler(ls=['-', '--', ':', '-.'])
+
+ax6b.set_prop_cycle(custom_cycler)
+
+for idx, contour in enumerate(all_contours_y):
+    for i in range( len(contour) ):
+        ax6b.plot(contour[i][:, 1], contour[i][:, 0], c=cycle[idx],
+                  linewidth=0.5, label=str(idx))
+ax6b.legend()
+ax6b.grid(False)
+ax6b.axes.get_yaxis().set_visible(False)
+ax6b.axes.get_xaxis().set_visible(False)
+divider6b = make_axes_locatable(ax6b)
+cax6b = divider6b.append_axes("right", size="5%", pad=0.05)
+cbar_6b = fig6.colorbar(im6b, cax=cax6b)
+cbar_6b.ax.set_title('[MPa]', fontsize='small')
+
diff --git a/examples/benchmarks/8/skull_mask_bm8_dx_1mm.mat b/examples/benchmarks/8/skull_mask_bm8_dx_1mm.mat
new file mode 100644
index 000000000..ae7976e9b
Binary files /dev/null and b/examples/benchmarks/8/skull_mask_bm8_dx_1mm.mat differ
diff --git a/examples/benchmarks/9/ph1-bm9-sc1.ipynb b/examples/benchmarks/9/ph1-bm9-sc1.ipynb
new file mode 100644
index 000000000..7a1924f77
--- /dev/null
+++ b/examples/benchmarks/9/ph1-bm9-sc1.ipynb
@@ -0,0 +1,623 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyMuXcrxBoxXiaqNG02ySogN",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/djps/k-wave-python/blob/benchmarks/examples/benchmarks/9/ph1-bm9-sc1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Tke5dl-DsUsg",
+        "outputId": "a1481320-f31c-4a42-f01a-8623adcd77ab"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/waltsims/k-wave-python\n",
+            "  Cloning https://github.com/waltsims/k-wave-python to /tmp/pip-req-build-9jon34lv\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/waltsims/k-wave-python /tmp/pip-req-build-9jon34lv\n",
+            "  Resolved https://github.com/waltsims/k-wave-python to commit d8f56f34eea9d1e68f72bce3708a9fc55f2fb71f\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: beartype==0.16.4 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (0.16.4)\n",
+            "Requirement already satisfied: deepdiff==6.7.1 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (6.7.1)\n",
+            "Requirement already satisfied: h5py==3.10.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.10.0)\n",
+            "Requirement already satisfied: matplotlib==3.8.3 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (3.8.3)\n",
+            "Requirement already satisfied: nptyping==2.5.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (2.5.0)\n",
+            "Requirement already satisfied: numpy<1.27.0,>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.25.2)\n",
+            "Requirement already satisfied: opencv-python==4.9.0.80 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (4.9.0.80)\n",
+            "Requirement already satisfied: scipy==1.12.0 in /usr/local/lib/python3.10/dist-packages (from k-Wave-python==0.3.2) (1.12.0)\n",
+            "Requirement already satisfied: ordered-set<4.2.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from deepdiff==6.7.1->k-Wave-python==0.3.2) (4.1.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (4.49.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (23.2)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.8.3->k-Wave-python==0.3.2) (2.8.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib==3.8.3->k-Wave-python==0.3.2) (1.16.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/waltsims/k-wave-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from copy import deepcopy\n",
+        "import requests\n",
+        "import shutil\n",
+        "\n",
+        "import logging\n",
+        "import sys\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+        "from cycler import cycler\n",
+        "\n",
+        "import h5py\n",
+        "\n",
+        "from skimage import measure\n",
+        "from skimage.segmentation import find_boundaries\n",
+        "from scipy.interpolate import interpn\n",
+        "\n",
+        "from kwave.data import Vector\n",
+        "from kwave.utils.kwave_array import kWaveArray\n",
+        "from kwave.utils.checks import check_stability\n",
+        "from kwave.kgrid import kWaveGrid\n",
+        "from kwave.kmedium import kWaveMedium\n",
+        "from kwave.ksource import kSource\n",
+        "from kwave.ksensor import kSensor\n",
+        "from kwave.utils.signals import create_cw_signals\n",
+        "from kwave.utils.filters import extract_amp_phase\n",
+        "from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG\n",
+        "\n",
+        "from kwave.options.simulation_options import SimulationOptions\n",
+        "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+        "\n",
+        "# create logger\n",
+        "logger = logging.getLogger(__name__)\n",
+        "logger.setLevel(logging.DEBUG)\n",
+        "# create console and file handlers and set level to debug\n",
+        "ch = logging.StreamHandler(sys.stdout)\n",
+        "ch.setLevel(logging.DEBUG)\n",
+        "fh = logging.FileHandler(filename='runner.log')\n",
+        "fh.setLevel(logging.DEBUG)\n",
+        "# create formatter\n",
+        "formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')\n",
+        "# add formatter to ch, fh\n",
+        "ch.setFormatter(formatter)\n",
+        "fh.setFormatter(formatter)\n",
+        "# add ch, fh to logger\n",
+        "logger.addHandler(ch)\n",
+        "logger.addHandler(fh)\n",
+        "# propagate\n",
+        "ch.propagate = True\n",
+        "fh.propagate = True\n",
+        "logger.propagate = True\n",
+        "\n",
+        "verbose: bool = True\n",
+        "savePlotting: bool = True\n",
+        "useMaxTimeStep: bool = True\n",
+        "\n",
+        "tag = 'bm8'\n",
+        "res = '1mm'\n",
+        "\n",
+        "url = 'https://raw.githubusercontent.com/djps/k-wave-python/benchmarks/examples/benchmarks/9/skull_mask_bm8_dx_1mm.mat'\n",
+        "mask_filename = requests.get(url, stream=True)\n",
+        "mask_filename.raw.decode_content = True\n",
+        "with open(\"temp.h5\", \"wb\") as _fh:\n",
+        "    shutil.copyfileobj(mask_filename.raw, _fh)\n",
+        "\n",
+        "data = h5py.File(\"temp.h5\", 'r')\n",
+        "\n",
+        "# is given in millimetres\n",
+        "dx = data['dx'][:].item()\n",
+        "\n",
+        "# scale to metres\n",
+        "dx = dx / 1000.0\n",
+        "dy = dx\n",
+        "dz = dx\n",
+        "\n",
+        "xi = np.squeeze(np.asarray(data['xi'][:]))\n",
+        "yi = np.squeeze(np.asarray(data['yi'][:]))\n",
+        "zi = np.squeeze(np.asarray(data['zi'][:]))\n",
+        "\n",
+        "matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]\n",
+        "\n",
+        "skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)\n",
+        "brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)\n",
+        "\n",
+        "skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')\n",
+        "brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')\n",
+        "\n",
+        "water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))\n",
+        "water_mask = water_mask.astype(bool)\n",
+        "\n",
+        "skull_mask = np.swapaxes(skull_mask, 0, 2)\n",
+        "brain_mask = np.swapaxes(brain_mask, 0, 2)\n",
+        "water_mask = np.swapaxes(water_mask, 0, 2)\n",
+        "\n",
+        "Nx, Ny, Nz = skull_mask.shape\n",
+        "\n",
+        "focus = int(64 / data['dx'][:].item())\n",
+        "\n",
+        "focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, focus]\n",
+        "\n",
+        "bowl_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]\n",
+        "\n",
+        "disc_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]"
+      ],
+      "metadata": {
+        "id": "8yA1bMzLsafy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# =========================================================================\n",
+        "# DEFINE THE MATERIAL PROPERTIES\n",
+        "# =========================================================================\n",
+        "\n",
+        "# water\n",
+        "sound_speed = 1500.0 * np.ones(skull_mask.shape)\n",
+        "density = 1000.0 * np.ones(skull_mask.shape)\n",
+        "alpha_coeff = np.zeros(skull_mask.shape)\n",
+        "\n",
+        "# non-dispersive\n",
+        "alpha_power = 2.0\n",
+        "\n",
+        "# skull\n",
+        "sound_speed[skull_mask] = 2800.0\n",
+        "density[skull_mask] = 1850.0\n",
+        "alpha_coeff[skull_mask] = 4.0\n",
+        "\n",
+        "# brain\n",
+        "sound_speed[brain_mask] = 1560.0\n",
+        "density[brain_mask] = 1040.0\n",
+        "alpha_coeff[brain_mask] = 0.3\n",
+        "\n",
+        "c0_min = np.min(sound_speed.flatten())\n",
+        "c0_max = np.min(sound_speed.flatten())\n",
+        "\n",
+        "medium = kWaveMedium(\n",
+        "    sound_speed=sound_speed,\n",
+        "    density=density,\n",
+        "    alpha_coeff=alpha_coeff,\n",
+        "    alpha_power=alpha_power\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "oD3h4kmosyU7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# bowl radius of curvature [m]\n",
+        "source_roc = 64.0e-3\n",
+        "\n",
+        "# as we will use the bowl element this has to be a int or float\n",
+        "diameters = 64.0e-3\n",
+        "\n",
+        "# frequency [Hz]\n",
+        "freq = 500e3\n",
+        "\n",
+        "# source pressure [Pa]\n",
+        "source_amp = np.array([60e3])\n",
+        "\n",
+        "# phase [rad]\n",
+        "source_phase = np.array([0.0])"
+      ],
+      "metadata": {
+        "id": "Jd73574-rfzi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# wavelength\n",
+        "k_min = c0_min / freq\n",
+        "\n",
+        "# points per wavelength\n",
+        "ppw = k_min / dx\n",
+        "\n",
+        "# number of periods to record\n",
+        "record_periods: int = 3\n",
+        "\n",
+        "# compute points per period\n",
+        "ppp: int = 20\n",
+        "\n",
+        "# CFL number determines time step\n",
+        "cfl = (ppw / ppp)"
+      ],
+      "metadata": {
+        "id": "FZPiOBwMr5QO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "grid_size_points = Vector([Nx, Ny, Nz])\n",
+        "\n",
+        "grid_spacing_meters = Vector([dx, dy, dz])\n",
+        "\n",
+        "# create the k-space grid\n",
+        "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)"
+      ],
+      "metadata": {
+        "id": "DCdgqPsRsCxW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# compute corresponding time stepping\n",
+        "dt = 1.0 / (ppp * freq)\n",
+        "\n",
+        "# compute corresponding time stepping\n",
+        "dt = (c0_min / c0_max) / (float(ppp) * freq)\n",
+        "\n",
+        "dt_stability_limit = check_stability(kgrid, medium)\n",
+        "\n",
+        "if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and\n",
+        "    (dt_stability_limit < dt)):\n",
+        "    dt_old = dt\n",
+        "    ppp = np.ceil( 1.0 / (dt_stability_limit * freq) )\n",
+        "    dt = 1.0 / (ppp * freq)\n",
+        "    if verbose:\n",
+        "        logger.info(\"updated dt\")\n",
+        "else:\n",
+        "    pass\n",
+        "\n",
+        "# calculate the number of time steps to reach steady state\n",
+        "t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min\n",
+        "\n",
+        "# create the time array using an integer number of points per period\n",
+        "Nt = round(t_end / dt)\n",
+        "\n",
+        "# make time array\n",
+        "kgrid.setTime(Nt, dt)\n",
+        "\n",
+        "# calculate the actual CFL after adjusting for dt\n",
+        "cfl_actual = 1.0 / (dt * freq)"
+      ],
+      "metadata": {
+        "id": "PSNLXqyesHlf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# create empty kWaveArray this specfies the transducer properties\n",
+        "karray = kWaveArray(bli_tolerance=0.01,\n",
+        "                    upsampling_rate=16,\n",
+        "                    single_precision=True)\n",
+        "\n",
+        "# set bowl position and orientation\n",
+        "bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),\n",
+        "            kgrid.y_vec[bowl_coords[1]].item(),\n",
+        "            kgrid.z_vec[bowl_coords[2]].item()]\n",
+        "\n",
+        "focus_pos = [kgrid.x_vec[focus_coords[0]].item(),\n",
+        "             kgrid.y_vec[focus_coords[1]].item(),\n",
+        "             kgrid.z_vec[focus_coords[2]].item()]\n",
+        "\n",
+        "# add bowl shaped element\n",
+        "karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)\n",
+        "\n",
+        "# create time varying source\n",
+        "source_sig = create_cw_signals(np.squeeze(kgrid.t_array),\n",
+        "                               freq,\n",
+        "                               source_amp,\n",
+        "                               source_phase)\n",
+        "\n",
+        "# make a source object.\n",
+        "source = kSource()\n",
+        "\n",
+        "# assign binary mask using the karray\n",
+        "source.p_mask = karray.get_array_binary_mask(kgrid)\n",
+        "\n",
+        "# assign source pressure output in time\n",
+        "source.p = karray.get_distributed_source_signal(kgrid, source_sig)"
+      ],
+      "metadata": {
+        "id": "ZJZDYFA0sIte"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sensor = kSensor()\n",
+        "\n",
+        "# set sensor mask: the mask says at which points data should be recorded\n",
+        "sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)\n",
+        "\n",
+        "# set the record type: record the pressure waveform\n",
+        "sensor.record = ['p_max', 'p_min']\n",
+        "\n",
+        "# record the final few periods when the field is in steady state\n",
+        "sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1"
+      ],
+      "metadata": {
+        "id": "7Y_mZ3kYsQfI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "DATA_CAST = 'single'\n",
+        "DATA_PATH = './'\n",
+        "\n",
+        "input_filename = tag + '_' + '_' + res + '_input.h5'\n",
+        "output_filename = tag + '_' + '_' + res + '_output.h5'\n",
+        "\n",
+        "# options for writing to file, but not doing simulations\n",
+        "simulation_options = SimulationOptions(\n",
+        "    data_cast=DATA_CAST,\n",
+        "    data_recast=True,\n",
+        "    save_to_disk=True,\n",
+        "    input_filename=input_filename,\n",
+        "    output_filename=output_filename,\n",
+        "    save_to_disk_exit=False,\n",
+        "    data_path=DATA_PATH,\n",
+        "    pml_inside=False)\n",
+        "\n",
+        "execution_options = SimulationExecutionOptions(\n",
+        "    is_gpu_simulation=True,\n",
+        "    delete_data=False,\n",
+        "    verbose_level=2)"
+      ],
+      "metadata": {
+        "id": "UGq4y5L2sVTT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sensor_data = kspaceFirstOrder3DG(\n",
+        "    medium=medium,\n",
+        "    kgrid=kgrid,\n",
+        "    source=source,\n",
+        "    sensor=sensor,\n",
+        "    simulation_options=simulation_options,\n",
+        "    execution_options=execution_options)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VS-cDCOAsuHP",
+        "outputId": "9c4b53ee-c4f5-4522-8042-f2073a3bcd35"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Highest prime factors in each dimension are [211 191  41]\n",
+            "WARNING:root:Use dimension sizes with lower prime factors to improve speed\n",
+            "WARNING:root:DeprecationWarning: Attributes will soon be typed when saved and not saved \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "┌───────────────────────────────────────────────────────────────┐\n",
+            "│                  kspaceFirstOrder-CUDA v1.3                   │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Git hash:            468dc31c2842a7df5f2a07c3a13c16c9b0b2b770 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Reading simulation configuration:                        Done │\n",
+            "│ File format version:                                      1.2 │\n",
+            "│ Selected GPU device id:                                     0 │\n",
+            "│ GPU device name:                                     Tesla T4 │\n",
+            "│ Number of CPU threads:                                      2 │\n",
+            "│ Processor name:                Intel(R) Xeon(R) CPU @ 2.20GHz │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                      Simulation details                       │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Domain dimensions:                            211 x 191 x 246 │\n",
+            "│ Medium type:                                               3D │\n",
+            "│ Simulation time steps:                                   1709 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Input file:  ./bm8__1mm_input.h5                              │\n",
+            "│ Input file:  ./bm8__1mm_output.h5                             │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Compression level:                                          0 │\n",
+            "│ Print progress interval:                                   5% │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Medium details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Wave propagation:                                      Linear │\n",
+            "│ Absorption type:                                    Power law │\n",
+            "│ Medium parameters:                              Heterogeneous │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Source details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure source:                                              │\n",
+            "│ + Source type:                                           Many │\n",
+            "│ + Source condition:                                  Additive │\n",
+            "│ + Memory usage:                                     3121.85MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Sensor details                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sensor mask type:                                       Index │\n",
+            "│ Sampling begins at time step:                            1650 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Pressure sensor p_raw                                         │\n",
+            "│ + File usage:                                       1689.47MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                        Initialization                         │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Memory allocation:                                       Done │\n",
+            "│ Data loading:                                                 │\n",
+            "│ + Reading input file:                                    Done │\n",
+            "│ + Creating output file:                                  Done │\n",
+            "│ Elapsed time:                                          18.23s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ FFT plans creation:                                      Done │\n",
+            "│ Pre-processing phase:                                    Done │\n",
+            "│ Elapsed time:                                           0.46s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                    Computational resources                    │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Current host memory in use:                            4394MB │\n",
+            "│ Current device memory in use:                          4561MB │\n",
+            "│ Expected output file size:                             1689MB │\n",
+            "│ CUDA solver grid size [blocks x threads]:           320 x 256 │\n",
+            "│ CUDA sampler grid size [blocks x threads]:          320 x 256 │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                          Simulation                           │\n",
+            "├──────────┬────────────────┬──────────────┬────────────────────┤\n",
+            "│ Progress │  Elapsed time  │  Time to go  │  Est. finish time  │\n",
+            "├──────────┼────────────────┼──────────────┼────────────────────┤\n",
+            "│     0%   │        0.001s  │      1.176s  │  04/03/24 12:33:18 │\n",
+            "│     5%   │        6.654s  │    124.063s  │  04/03/24 12:35:28 │\n",
+            "│    10%   │       13.900s  │    124.208s  │  04/03/24 12:35:35 │\n",
+            "│    15%   │       21.277s  │    119.660s  │  04/03/24 12:35:38 │\n",
+            "│    20%   │       28.619s  │    113.974s  │  04/03/24 12:35:39 │\n",
+            "│    25%   │       36.098s  │    107.704s  │  04/03/24 12:35:40 │\n",
+            "│    30%   │       43.543s  │    101.233s  │  04/03/24 12:35:42 │\n",
+            "│    35%   │       51.124s  │     94.495s  │  04/03/24 12:35:42 │\n",
+            "│    40%   │       58.665s  │     87.698s  │  04/03/24 12:35:43 │\n",
+            "│    45%   │       66.346s  │     80.717s  │  04/03/24 12:35:44 │\n",
+            "│    50%   │       73.987s  │     73.728s  │  04/03/24 12:35:44 │\n",
+            "│    55%   │       81.675s  │     66.659s  │  04/03/24 12:35:45 │\n",
+            "│    60%   │       89.494s  │     59.430s  │  04/03/24 12:35:46 │\n",
+            "│    65%   │       97.270s  │     52.221s  │  04/03/24 12:35:47 │\n",
+            "│    70%   │      105.181s  │     44.864s  │  04/03/24 12:35:47 │\n",
+            "│    75%   │      113.039s  │     37.533s  │  04/03/24 12:35:47 │\n",
+            "│    80%   │      121.033s  │     30.059s  │  04/03/24 12:35:48 │\n",
+            "│    85%   │      128.982s  │     22.621s  │  04/03/24 12:35:48 │\n",
+            "│    90%   │      137.074s  │     15.043s  │  04/03/24 12:35:49 │\n",
+            "│    95%   │      145.109s  │      7.501s  │  04/03/24 12:35:49 │\n",
+            "├──────────┴────────────────┴──────────────┴────────────────────┤\n",
+            "│ Elapsed time:                                         209.14s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Sampled data post-processing:                            Done │\n",
+            "│ Elapsed time:                                           0.00s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                            Summary                            │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Peak host memory in use:                               4450MB │\n",
+            "│ Peak device memory in use:                             4561MB │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│ Total execution time:                                 230.96s │\n",
+            "├───────────────────────────────────────────────────────────────┤\n",
+            "│                       End of computation                      │\n",
+            "└───────────────────────────────────────────────────────────────┘\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# sampling frequency\n",
+        "fs = 1.0 / kgrid.dt\n",
+        "\n",
+        "# get Fourier coefficients\n",
+        "#amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')\n",
+        "\n",
+        "# reshape data: matlab uses Fortran ordering\n",
+        "p = np.reshape(sensor_data['p_max'].T, (Nx, Ny, Nz), order='F')\n",
+        "\n",
+        "# # get maximum pressure\n",
+        "# pmax = np.nanmax(p)\n",
+        "\n",
+        "# # get location of maximum pressure\n",
+        "# max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='F')\n",
+        "\n",
+        "p_brain = np.empty_like(p)\n",
+        "p_brain.fill(np.nan)\n",
+        "p_brain[brain_mask] = p[brain_mask]\n",
+        "pmax_brain = np.nanmax(p_brain)\n",
+        "max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='F')\n",
+        "\n",
+        "fig3, (ax3a, ax3b) = plt.subplots(1, 2)\n",
+        "im3a = ax3a.pcolormesh(np.squeeze(kgrid.y_vec), np.squeeze(kgrid.z_vec), p[max_loc_brain[0], :, :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "ax3a.grid(False)\n",
+        "ax3a.axes.get_yaxis().set_visible(False)\n",
+        "ax3a.axes.get_xaxis().set_visible(False)\n",
+        "divider3a = make_axes_locatable(ax3a)\n",
+        "cax3a = divider3a.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3a = fig3.colorbar(im3a, cax=cax3a)\n",
+        "cbar_3a.ax.set_title('[MPa]', fontsize='small')\n",
+        "ax3a.invert_yaxis()\n",
+        "im3b = ax3b.pcolormesh(np.squeeze(kgrid.x_vec), np.squeeze(kgrid.z_vec), p[:, max_loc_brain[1], :].T / 1e6,\n",
+        "                       shading='gouraud', cmap='viridis')\n",
+        "ax3b.grid(False)\n",
+        "ax3b.axes.get_yaxis().set_visible(False)\n",
+        "ax3b.axes.get_xaxis().set_visible(False)\n",
+        "divider3b = make_axes_locatable(ax3b)\n",
+        "cax3b = divider3b.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+        "cbar_3b = fig3.colorbar(im3a, cax=cax3b)\n",
+        "cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})\n",
+        "ax3b.invert_yaxis()\n",
+        "plt.tight_layout(pad=1.2)"
+      ],
+      "metadata": {
+        "id": "PgH2bG-oYK_z"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/examples/benchmarks/9/ph1-bm9-sc1.py b/examples/benchmarks/9/ph1-bm9-sc1.py
new file mode 100644
index 000000000..918e98fb8
--- /dev/null
+++ b/examples/benchmarks/9/ph1-bm9-sc1.py
@@ -0,0 +1,322 @@
+import numpy as np
+
+from copy import deepcopy
+import requests
+import shutil
+
+import logging
+import sys
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from cycler import cycler
+
+import h5py
+
+from skimage import measure
+from skimage.segmentation import find_boundaries
+from scipy.interpolate import interpn
+
+from kwave.data import Vector
+from kwave.utils.kwave_array import kWaveArray
+from kwave.utils.checks import check_stability
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksource import kSource
+from kwave.ksensor import kSensor
+from kwave.utils.signals import create_cw_signals
+from kwave.utils.filters import extract_amp_phase
+from kwave.kspaceFirstOrder3D import kspaceFirstOrder3DG
+
+from kwave.options.simulation_options import SimulationOptions
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+
+# create logger
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+# create console and file handlers and set level to debug
+ch = logging.StreamHandler(sys.stdout)
+ch.setLevel(logging.DEBUG)
+fh = logging.FileHandler(filename='runner.log')
+fh.setLevel(logging.DEBUG)
+# create formatter
+formatter = logging.Formatter('%(asctime)s | %(name)s | %(levelname)s | %(message)s')
+# add formatter to ch, fh
+ch.setFormatter(formatter)
+fh.setFormatter(formatter)
+# add ch, fh to logger
+logger.addHandler(ch)
+logger.addHandler(fh)
+# propagate
+ch.propagate = True
+fh.propagate = True
+logger.propagate = True
+
+verbose: bool = True
+savePlotting: bool = True
+useMaxTimeStep: bool = True
+
+tag = 'bm8'
+res = '1mm'
+
+url = 'https://raw.githubusercontent.com/djps/k-wave-python/benchmarks/examples/benchmarks/9/skull_mask_bm9_dx_1mm.mat'
+mask_filename = requests.get(url, stream=True)
+mask_filename.raw.decode_content = True
+with open("temp.h5", "wb") as _fh:
+    shutil.copyfileobj(mask_filename.raw, _fh)
+
+data = h5py.File("temp.h5", 'r')
+
+# is given in millimetres
+dx = data['dx'][:].item()
+
+# scale to metres
+dx = dx / 1000.0
+dy = dx
+dz = dx
+
+xi = np.squeeze(np.asarray(data['xi'][:]))
+yi = np.squeeze(np.asarray(data['yi'][:]))
+zi = np.squeeze(np.asarray(data['zi'][:]))
+
+matlab_shape = np.shape(xi)[0], np.shape(yi)[0], np.shape(zi)[0]
+
+skull_mask = np.squeeze(data['skull_mask'][:]).astype(bool)
+brain_mask = np.squeeze(data['brain_mask'][:]).astype(bool)
+
+skull_mask = np.reshape(skull_mask.flatten(), matlab_shape, order='F')
+brain_mask = np.reshape(brain_mask.flatten(), matlab_shape, order='F')
+
+water_mask = np.ones(skull_mask.shape, dtype=int) - (skull_mask.astype(int) + brain_mask.astype(int))
+water_mask = water_mask.astype(bool)
+
+skull_mask = np.swapaxes(skull_mask, 0, 2)
+brain_mask = np.swapaxes(brain_mask, 0, 2)
+water_mask = np.swapaxes(water_mask, 0, 2)
+
+Nx, Ny, Nz = skull_mask.shape
+
+focus = int(64 / data['dx'][:].item())
+
+focus_coords = [(Nx - 1) // 2, (Ny - 1) // 2, focus]
+
+bowl_coords = [(Nx - 1) // 2, (Ny - 1) // 2, 0]
+
+disc_coords = [(Nx-1) // 2, (Ny-1) // 2, 0]
+
+# =========================================================================
+# DEFINE THE MATERIAL PROPERTIES
+# =========================================================================
+
+# water
+sound_speed = 1500.0 * np.ones(skull_mask.shape)
+density = 1000.0 * np.ones(skull_mask.shape)
+alpha_coeff = np.zeros(skull_mask.shape)
+
+# non-dispersive
+alpha_power = 2.0
+
+# skull
+sound_speed[skull_mask] = 2800.0
+density[skull_mask] = 1850.0
+alpha_coeff[skull_mask] = 4.0
+
+# brain
+sound_speed[brain_mask] = 1560.0
+density[brain_mask] = 1040.0
+alpha_coeff[brain_mask] = 0.3
+
+c0_min = np.min(sound_speed.flatten())
+c0_max = np.min(sound_speed.flatten())
+
+medium = kWaveMedium(
+    sound_speed=sound_speed,
+    density=density,
+    alpha_coeff=alpha_coeff,
+    alpha_power=alpha_power
+)
+
+# bowl radius of curvature [m]
+source_roc = 64.0e-3
+
+# as we will use the bowl element this has to be a int or float
+diameters = 64.0e-3
+
+# frequency [Hz]
+freq = 500e3
+
+# source pressure [Pa]
+source_amp = np.array([60e3])
+
+# phase [rad]
+source_phase = np.array([0.0])
+
+# wavelength
+k_min = c0_min / freq
+
+# points per wavelength
+ppw = k_min / dx
+
+# number of periods to record
+record_periods: int = 3
+
+# compute points per period
+ppp: int = 20
+
+# CFL number determines time step
+cfl = (ppw / ppp)
+
+grid_size_points = Vector([Nx, Ny, Nz])
+
+grid_spacing_meters = Vector([dx, dy, dz])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+# compute corresponding time stepping
+dt = 1.0 / (ppp * freq)
+
+# compute corresponding time stepping
+dt = (c0_min / c0_max) / (float(ppp) * freq)
+
+dt_stability_limit = check_stability(kgrid, medium)
+
+if (useMaxTimeStep and (not np.isfinite(dt_stability_limit)) and
+    (dt_stability_limit < dt)):
+    dt_old = dt
+    ppp = np.ceil( 1.0 / (dt_stability_limit * freq) )
+    dt = 1.0 / (ppp * freq)
+    if verbose:
+        logger.info("updated dt")
+else:
+    pass
+
+# calculate the number of time steps to reach steady state
+t_end = np.sqrt(kgrid.x_size**2 + kgrid.y_size**2) / c0_min
+
+# create the time array using an integer number of points per period
+Nt = round(t_end / dt)
+
+# make time array
+kgrid.setTime(Nt, dt)
+
+# calculate the actual CFL after adjusting for dt
+cfl_actual = 1.0 / (dt * freq)
+
+# create empty kWaveArray this specfies the transducer properties
+karray = kWaveArray(bli_tolerance=0.01,
+                    upsampling_rate=16,
+                    single_precision=True)
+
+# set bowl position and orientation
+bowl_pos = [kgrid.x_vec[bowl_coords[0]].item(),
+            kgrid.y_vec[bowl_coords[1]].item(),
+            kgrid.z_vec[bowl_coords[2]].item()]
+
+focus_pos = [kgrid.x_vec[focus_coords[0]].item(),
+             kgrid.y_vec[focus_coords[1]].item(),
+             kgrid.z_vec[focus_coords[2]].item()]
+
+# add bowl shaped element
+karray.add_bowl_element(bowl_pos, source_roc, diameters, focus_pos)
+
+# create time varying source
+source_sig = create_cw_signals(np.squeeze(kgrid.t_array),
+                               freq,
+                               source_amp,
+                               source_phase)
+
+# make a source object.
+source = kSource()
+
+# assign binary mask using the karray
+source.p_mask = karray.get_array_binary_mask(kgrid)
+
+# assign source pressure output in time
+source.p = karray.get_distributed_source_signal(kgrid, source_sig)
+
+sensor = kSensor()
+
+# set sensor mask: the mask says at which points data should be recorded
+sensor.mask = np.ones((Nx, Ny, Nz), dtype=bool)
+
+# set the record type: record the pressure waveform
+sensor.record = ['p_max', 'p_min']
+
+# record the final few periods when the field is in steady state
+sensor.record_start_index = kgrid.Nt - record_periods * ppp + 1
+
+DATA_CAST = 'single'
+DATA_PATH = './'
+
+input_filename = tag + '_' + '_' + res + '_input.h5'
+output_filename = tag + '_' + '_' + res + '_output.h5'
+
+# options for writing to file, but not doing simulations
+simulation_options = SimulationOptions(
+    data_cast=DATA_CAST,
+    data_recast=True,
+    save_to_disk=True,
+    input_filename=input_filename,
+    output_filename=output_filename,
+    save_to_disk_exit=False,
+    data_path=DATA_PATH,
+    pml_inside=False)
+
+execution_options = SimulationExecutionOptions(
+    is_gpu_simulation=True,
+    delete_data=False,
+    verbose_level=2)
+
+sensor_data = kspaceFirstOrder3DG(
+    medium=medium,
+    kgrid=kgrid,
+    source=source,
+    sensor=sensor,
+    simulation_options=simulation_options,
+    execution_options=execution_options)
+
+# sampling frequency
+fs = 1.0 / kgrid.dt
+
+# get Fourier coefficients
+#amp, _, _ = extract_amp_phase(sensor_data['p'].T, fs, freq, dim=1, fft_padding=1, window='Rectangular')
+
+# reshape data: matlab uses Fortran ordering
+p = np.reshape(sensor_data['p_max'].T, (Nx, Ny, Nz), order='F')
+
+# # get maximum pressure
+# pmax = np.nanmax(p)
+
+# # get location of maximum pressure
+# max_loc = np.unravel_index(np.nanargmax(p), p.shape, order='F')
+
+p_brain = np.empty_like(p)
+p_brain.fill(np.nan)
+p_brain[brain_mask] = p[brain_mask]
+pmax_brain = np.nanmax(p_brain)
+max_loc_brain = np.unravel_index(np.nanargmax(p_brain), p.shape, order='F')
+
+fig3, (ax3a, ax3b) = plt.subplots(1, 2)
+im3a = ax3a.pcolormesh(np.squeeze(kgrid.y_vec), np.squeeze(kgrid.z_vec), p[max_loc_brain[0], :, :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+ax3a.grid(False)
+ax3a.axes.get_yaxis().set_visible(False)
+ax3a.axes.get_xaxis().set_visible(False)
+divider3a = make_axes_locatable(ax3a)
+cax3a = divider3a.append_axes("right", size="5%", pad=0.05)
+cbar_3a = fig3.colorbar(im3a, cax=cax3a)
+cbar_3a.ax.set_title('[MPa]', fontsize='small')
+ax3a.invert_yaxis()
+im3b = ax3b.pcolormesh(np.squeeze(kgrid.x_vec), np.squeeze(kgrid.z_vec), p[:, max_loc_brain[1], :].T / 1e6,
+                       shading='gouraud', cmap='viridis')
+ax3b.grid(False)
+ax3b.axes.get_yaxis().set_visible(False)
+ax3b.axes.get_xaxis().set_visible(False)
+divider3b = make_axes_locatable(ax3b)
+cax3b = divider3b.append_axes("right", size="5%", pad=0.05)
+cbar_3b = fig3.colorbar(im3a, cax=cax3b)
+cbar_3b.ax.set_title('[MPa]', fontdict={'fontsize': 8})
+ax3b.invert_yaxis()
+plt.tight_layout(pad=1.2)
+
+plt.show
diff --git a/examples/benchmarks/9/skull_mask_bm9_dx_1mm.mat b/examples/benchmarks/9/skull_mask_bm9_dx_1mm.mat
new file mode 100644
index 000000000..60b943f11
Binary files /dev/null and b/examples/benchmarks/9/skull_mask_bm9_dx_1mm.mat differ
diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md
new file mode 100644
index 000000000..ed0a529be
--- /dev/null
+++ b/examples/benchmarks/README.md
@@ -0,0 +1,11 @@
+The examples in this folder are from:
+
+> Jean-Francois Aubry, Oscar Bates, Christian Boehm, Kim Butts Pauly, Douglas Christensen, Carlos Cueto, Pierre Gelat, Lluis Guasch, Jiri Jaros, Yun Jing, Rebecca Jones, Ningrui Li, 
+Patrick Marty, Hazael Montanaro, Esra Neufeld, Samuel Pichardo, Gianmarco Pinton, Aki Pulkkinen, Antonio Stanziola, Axel Thielscher, Bradley Treeby, 
+Elwin van 't Wout. "Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave model" _J. Acoust. Soc. Am._ **152**, 1003–1019 (2022)
+https://doi.org/10.1121/10.0013426
+
+The data sets (v1.0) can be accessed from Zenodo under the Creative Commons Attribution 4.0 International license. 
+https://doi.org/10.5281/zenodo.6020543
+
+
diff --git a/kwave/utils/filters.py b/kwave/utils/filters.py
index 50aab667c..eea7ded44 100644
--- a/kwave/utils/filters.py
+++ b/kwave/utils/filters.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 import scipy
-from scipy.fftpack import fft, ifft, ifftshift, fftshift
+from scipy.fft import fft, ifft, ifftshift, fftshift
 from scipy.signal import lfilter, convolve
 
 from .checks import is_number
@@ -29,26 +29,32 @@ def single_sided_correction(func_fft: np.ndarray, fft_len: int, dim: int) -> np.
     Returns:
         The corrected FFT of the function.
     """
+
+    # get data type of data
+    x = np.real(func_fft)[0, 0]
+    # convert 2 into the same data type
+    two = x.dtype.type(2)
+
     if fft_len % 2:
         # odd FFT length switch dim case
         if dim == 0:
-            func_fft[1:, :] = func_fft[1:, :] * 2
+            func_fft[1:, :] = func_fft[1:, :] * two
         elif dim == 1:
-            func_fft[:, 1:] = func_fft[:, 1:] * 2
+            func_fft[:, 1:] = func_fft[:, 1:] * two
         elif dim == 2:
-            func_fft[:, :, 1:] = func_fft[:, :, 1:] * 2
+            func_fft[:, :, 1:] = func_fft[:, :, 1:] * two
         elif dim == 3:
-            func_fft[:, :, :, 1:] = func_fft[:, :, :, 1:] * 2
+            func_fft[:, :, :, 1:] = func_fft[:, :, :, 1:] * two
     else:
         # even FFT length
         if dim == 0:
-            func_fft[1:-1] = func_fft[1:-1] * 2
+            func_fft[1:-1] = func_fft[1:-1] * two
         elif dim == 1:
-            func_fft[:, 1:-1] = func_fft[:, 1:-1] * 2
+            func_fft[:, 1:-1] = func_fft[:, 1:-1] * two
         elif dim == 2:
-            func_fft[:, :, 1:-1] = func_fft[:, :, 1:-1] * 2
+            func_fft[:, :, 1:-1] = func_fft[:, :, 1:-1] * two
         elif dim == 3:
-            func_fft[:, :, :, 1:-1] = func_fft[:, :, :, 1:-1] * 2
+            func_fft[:, :, :, 1:-1] = func_fft[:, :, :, 1:-1] * two
 
     return func_fft
 
@@ -88,6 +94,9 @@ def spect(
     # check the size of the input
     sz = func.shape
 
+    # precision
+    dataType = type(func[0, 0])
+
     # check input isn't scalar
     if np.size(func) == 1:
         raise ValueError("Input signal cannot be scalar.")
@@ -98,7 +107,7 @@ def spect(
 
     # automatically set dimension to first non - singleton dimension
     if dim == "auto":
-        dim_index = 0
+        dim_index: int = 0
         while dim_index <= len(sz):
             if sz[dim_index] > 1:
                 dim = dim_index
@@ -121,7 +130,7 @@ def spect(
     # window the signal, reshaping the window to be in the correct direction
     win, coherent_gain = get_win(func_length, window, symmetric=False)
     win = np.reshape(win, tuple(([1] * dim + [func_length] + [1] * (len(sz) - 2))))
-    func = win * func
+    func = win.astype(dataType) * func
 
     # compute the fft using the defined FFT length, if fft_len >
     # func_length, the input signal is padded with zeros
@@ -149,7 +158,7 @@ def spect(
     f = np.arange(0, func_fft.shape[dim]) * Fs / fft_len
 
     # calculate the amplitude spectrum
-    func_as = np.abs(func_fft)
+    func_as = np.abs(func_fft).astype(dataType)
 
     # calculate the phase spectrum
     func_ps = np.angle(func_fft)
@@ -184,6 +193,8 @@ def extract_amp_phase(
 
     """
 
+    dataType = type(data[0, 0])
+
     # check for the dim input
     if dim == "auto":
         dim = num_dim(data)
@@ -198,13 +209,14 @@ def extract_amp_phase(
     win = np.reshape(win, [1] * (dim - 1) + [len(win)])
 
     # apply window to time dimension of input data
-    data = win * data
+    data = win.astype(dataType) * data
 
     # compute amplitude and phase spectra
     f, func_as, func_ps = spect(data, Fs, fft_len=fft_padding * data.shape[dim], dim=dim)
 
     # correct for coherent gain
     func_as = func_as / coherent_gain
+    func_as = func_as.astype(dataType)
 
     # find the index of the frequency component closest to source_freq
     _, f_index = find_closest(f, source_freq)