src.acoustools.BEM.Propagator

 1import torch
 2from torch import Tensor
 3
 4from vedo import Mesh
 5
 6from acoustools.Utilities import TOP_BOARD
 7from acoustools.BEM.Forward_models import compute_E
 8from acoustools.BEM.Gradients import BEM_forward_model_grad
 9
10
11def propagate_BEM(activations:Tensor,points:Tensor,scatterer:Mesh|None=None,board:Tensor|None=None,H:Tensor|None=None,
12                  E:Tensor|None=None,path:str="Media", use_cache_H: bool=True,print_lines:bool=False) ->Tensor:
13    '''
14    Propagates transducer phases to points using BEM\n
15    :param activations: Transducer hologram
16    :param points: Points to propagate to
17    :param scatterer: The mesh used (as a `vedo` `mesh` object)
18    :param board: Transducers to use, if `None` then uses `acoustools.Utilities.TOP_BOARD` 
19    :param H: Precomputed H - if None H will be computed
20    :param E: Precomputed E - if None E will be computed
21    :param path: path to folder containing `BEMCache/ `
22    :param use_cache_H: If True uses the cache system to load and save the H matrix. Default `True`
23    :param print_lines: if true prints messages detaling progress
24    :return pressure: complex pressure at points
25    '''
26    if board is None:
27        board = TOP_BOARD
28
29    if E is None:
30        if type(scatterer) == str:
31            scatterer = load_scatterer(scatterer)
32        E = compute_E(scatterer,points,board,H=H, path=path,use_cache_H=use_cache_H,print_lines=print_lines)
33    
34    out = E@activations
35    return out
36
37def propagate_BEM_pressure(activations:Tensor,points:Tensor,scatterer:Mesh|None=None,board:Tensor|None=None,H:
38                           Tensor|None=None,E:Tensor|None=None, path:str="Media",use_cache_H:bool=True, print_lines:bool=False) -> Tensor:
39    '''
40    Propagates transducer phases to points using BEM and returns absolute value of complex pressure\n
41    Equivalent to `torch.abs(propagate_BEM(activations,points,scatterer,board,H,E,path))` \n
42    :param activations: Transducer hologram
43    :param points: Points to propagate to
44    :param scatterer: The mesh used (as a `vedo` `mesh` object)
45    :param board: Transducers to use 
46    :param H: Precomputed H - if None H will be computed
47    :param E: Precomputed E - if None E will be computed 
48    :param path: path to folder containing `BEMCache/ `
49    
50    :param use_cache_H: If True uses the cache system to load and save the H matrix. Default `True`
51    :param print_lines: if true prints messages detaling progress
52    
53    :return pressure: real pressure at points
54    '''
55    if board is None:
56        board = TOP_BOARD
57
58    point_activations = propagate_BEM(activations,points,scatterer,board,H,E,path,use_cache_H=use_cache_H,print_lines=print_lines)
59    pressures =  torch.abs(point_activations)
60    return pressures
61
62def propagate_BEM_pressure_grad(activations: Tensor, points: Tensor,board: Tensor|None=None, scatterer:Mesh = None, 
63                                path:str='Media', Fx=None, Fy=None, Fz=None, cat=True):
64    '''
65    Propagates a hologram to pressure gradient at points\n
66    :param activations: Hologram to use
67    :param points: Points to propagate to
68    :param board: The Transducer array, default two 16x16 arrays
69    :param Fx: The forward model to us for Fx, if None it is computed using `forward_model_grad`. Default:`None`
70    :param Fy: The forward model to us for Fy, if None it is computed using `forward_model_grad`. Default:`None`
71    :param Fz: The forward model to us for Fz, if None it is computed using `forward_model_grad`. Default:`None`
72    :return: point velocity potential'
73    '''
74    
75    if Fx is None or Fy is None or Fz is None:
76        _Fx,_Fy,_Fz = BEM_forward_model_grad(points, scatterer ,board)
77        if Fx is None: Fx = _Fx
78        if Fy is None: Fy = _Fy
79        if Fz is None: Fz = _Fz
80    
81    Px = Fx@activations
82    Py = Fy@activations
83    Pz = Fz@activations
84
85    if cat: 
86        grad = torch.cat([Px, Py, Pz], dim=2)
87        return grad
88    return Px, Py, Pz