jwave.geometry
BLISensors
Band-limited interpolant (off-grid) sensors.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
positions |
Tuple of List of float
|
Sensor positions. |
required |
n |
Tuple of int
|
Grid size. |
required |
Attributes:
| Name | Type | Description |
|---|---|---|
positions |
Tuple[jnp.ndarray]
|
Sensor positions |
n |
Tuple[int]
|
Grid size. |
__call__(p, u, v)
Returns the values of the field p at the sensors positions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
p |
Field
|
The field to be sampled. |
required |
Medium
Bases: Module
summary
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
eqx |
_type_
|
description |
required |
Raises:
| Type | Description |
|---|---|
ValueError
|
description |
TypeError
|
description |
ValueError
|
description |
Returns:
| Name | Type | Description |
|---|---|---|
_type_ |
description |
int_pml_size: int
property
Returns the size of the PML layer as an integer
max_attenuation
property
Calculate and return the maximum attenuation.
This property uses the attenuation method/function and applies the amax
function from JAX's numpy (jnp) library to find the maximum attenuation value.
Returns:
| Type | Description |
|---|---|
|
The maximum attenuation value. |
max_density
property
Calculate and return the maximum density.
This property uses the density method/function and applies the amax
function from JAX's numpy (jnp) library to find the maximum density value.
Returns:
| Type | Description |
|---|---|
|
The maximum density value. |
max_sound_speed
property
Calculate and return the maximum sound speed.
This property uses the sound_speed method/function and applies the amax
function from JAX's numpy (jnp) library to find the maximum sound speed value.
Returns:
| Type | Description |
|---|---|
|
The maximum sound speed value. |
min_attenuation
property
Calculate and return the minimum attenuation.
This property uses the attenuation method/function and applies the amin
function from JAX's numpy (jnp) library to find the minimum attenuation value.
Returns:
| Type | Description |
|---|---|
|
The minimum attenuation value. |
min_density
property
Calculate and return the minimum density.
This property uses the density method/function and applies the amin
function from JAX's numpy (jnp) library to find the minimum density value.
Returns:
| Type | Description |
|---|---|
|
The minimum density value. |
min_sound_speed
property
Calculate and return the minimum sound speed.
This property uses the sound_speed method/function and applies the amin
function from JAX's numpy (jnp) library to find the minimum sound speed value.
Returns:
| Type | Description |
|---|---|
|
The minimum sound speed value. |
__infer_type_parameter__(*args, **kwargs)
classmethod
Inter the type parameter from the arguments. Defaults to FourierSeries if the parameters are all floats
__init_type_parameter__(t)
classmethod
Check whether the type parameters is valid.
Sensors
Sensors structure
Attributes:
| Name | Type | Description |
|---|---|---|
positions |
Tuple[List[int]]
|
sensors positions |
Example
x_pos = [10,20,30,40]
y_pos = [30,30,30,30]
sensors = geometry.Sensors(positions=(x_pos, y_pos))
__call__(p, u, rho)
Returns the values of the field u at the sensors positions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
u |
Field
|
The field to be sampled. |
required |
to_binary_mask(N)
Convert sensors to binary mask
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
N |
Tuple[int]
|
grid size |
required |
Returns:
| Type | Description |
|---|---|
|
jnp.ndarray: binary mask |
Sources
Sources structure
Attributes:
| Name | Type | Description |
|---|---|---|
positions |
Tuple[List[int]
|
source positions |
signals |
List[jnp.ndarray]
|
source signals |
dt |
float
|
time step |
domain |
Domain
|
domain |
Example
x_pos = [10,20,30,40]
y_pos = [30,30,30,30]
signal = jnp.sin(jnp.linspace(0,10,100))
signals = jnp.stack([signal]*4)
sources = geometry.Source(positions=(x_pos, y_pos), signals=signals)
to_binary_mask(N)
Convert sources to binary mask
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
N |
Tuple[int]
|
grid size |
required |
Returns:
| Type | Description |
|---|---|
|
jnp.ndarray: binary mask |
TimeAxis
Temporal vector to be used for acoustic simulation based on the pseudospectral method of k-Wave
Attributes:
| Name | Type | Description |
|---|---|---|
dt |
float
|
time step |
t_end |
float
|
simulation end time |
Nt
property
Returns the number of time steps
from_medium(medium, cfl=0.3, t_end=None)
staticmethod
Construct a TimeAxis object from kGrid and Medium
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
grid |
kGrid
|
|
required |
medium |
Medium
|
|
required |
cfl |
float
|
The CFL number. Defaults to 0.3. |
0.3
|
t_end |
[float]
|
The final simulation time. If None, it is automatically calculated as the time required to travel from one corner of the domain to the opposite one. |
None
|
to_array()
Returns the time-axis as an array
TimeHarmonicSource
dataclass
TimeHarmonicSource dataclass
Attributes:
| Name | Type | Description |
|---|---|---|
domain |
Domain
|
domain |
amplitude |
Field
|
The complex amplitude field of the sources |
omega |
float
|
The angular frequency of the sources |
on_grid(t=0.0)
Returns the complex field corresponding to the sources distribution at time $t$.
bli_function(x0, x, n, include_imag=False)
The function used to compute the band limited interpolation function.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x0 |
jnp.ndarray
|
Position of the sensors along the axis. |
required |
x |
jnp.ndarray
|
Grid positions. |
required |
n |
int
|
Size of the grid |
required |
include_imag |
bool
|
Include the imaginary component? |
False
|
Returns:
| Type | Description |
|---|---|
jnp.ndarray
|
jnp.ndarray: The values of the function at the grid positions. |
circ_mask(N, radius, centre)
Generate a 2D binary mask representing a circle within a 2D grid.
The mask is an ndarray of size N with 1s inside the circle (defined by a given centre and radius) and 0s outside.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
N |
Tuple[int, int]
|
The shape of the output mask (size of the grid). It should be in the format (x_size, y_size). |
required |
radius |
float
|
The radius of the circle. |
required |
centre |
Union[List[float], Tuple[float, float]]
|
The coordinates of the centre of the circle in the format (x, y). |
required |
Returns:
| Name | Type | Description |
|---|---|---|
mask |
np.ndarray
|
The 2D mask as a numpy ndarray of integers. The shape of the mask is N. Values inside the circle are 1, and values outside the circle are 0. |
fibonacci_sphere(n, radius, centre, cast_int=True)
Generate evenly distributed points on the surface of a sphere using the Fibonacci Sphere method.
n (int): The number of points to generate. radius (float): The radius of the sphere. centre (tuple or np.ndarray): The (x, y, z) coordinates of the center of the sphere. cast_int (bool, optional): If True, points will be rounded and converted to integers. Default is True.
x, y, z (tuple): The x, y, and z coordinates of the points on the sphere.
get_line_transducer(domain, position, width, angle=0)
Construct a line transducer (2D)
points_on_circle(n, radius, centre, cast_int=True, angle=0.0, max_angle=2 * np.pi)
Generate points on a circle.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n |
int
|
Number of points. |
required |
radius |
float
|
Radius of the circle. |
required |
centre |
tuple
|
Centre coordinates of the circle (x, y). |
required |
cast_int |
bool
|
If True, points will be rounded and converted to integers. Default is True. |
True
|
angle |
float
|
Starting angle in radians. Default is 0. |
0.0
|
max_angle |
float
|
Maximum angle to reach in radians. Default is 2*pi (full circle). |
2 * np.pi
|
Returns:
| Type | Description |
|---|---|
Tuple[List[float], List[float]]
|
x, y (tuple): Lists of x and y coordinates of the points. |
sphere_mask(N, radius, centre)
Generate a 3D binary mask representing a sphere within a 3D grid.
The mask is an ndarray of size N with 1s inside the sphere (defined by a given centre and radius) and 0s outside.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
N |
Tuple[int, int, int]
|
The shape of the output mask (size of the grid). It should be in the format (x_size, y_size, z_size). |
required |
radius |
float
|
The radius of the sphere. |
required |
centre |
Union[List[float], Tuple[float, float, float]]
|
The coordinates of the centre of the sphere in the format (x, y, z). |
required |
Returns:
| Name | Type | Description |
|---|---|---|
mask |
np.ndarray
|
The 3D mask as a numpy ndarray of integers. The shape of the mask is N. Values inside the sphere are 1, and values outside the sphere are 0. |
unit_fibonacci_sphere(samples=128)
Generate evenly distributed points on the surface of a unit sphere using the Fibonacci Sphere method.
From https://stackoverflow.com/questions/9600801/evenly-distributing-n-points-on-a-sphere
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
samples |
int
|
The number of points to generate. Default is 128. |
128
|
Returns:
| Name | Type | Description |
|---|---|---|
points |
list
|
A list of tuples representing the (x, y, z) coordinates of the points on the sphere. |