src.acoustools.Utilities.Targets 

 1from torch import Tensor
 2import torch
 3
 4from acoustools.Utilities.Setup import device
 5
 6def generate_gorkov_targets(N:int,B:int=1, max_val:float=-5, min_val:float=-5, log=True) -> Tensor:
 7    '''
 8    Generates a tensor of random negative Gor'kov potential values\n
 9    If `B=0` will return tensor with shape of `Nx1` else  will have shape `BxNx1`\n
10    :param N: Number of values per batch
11    :param B: Number of batches to produce
12    :param max_val: Maximum exponent of the value that can be generated. Default: `0`
13    :param min_val: Minimum exponent of the value that can be generated. Default: `-1e-4`
14    :return: tensor of values
15'''
16   
17    if B > 0:
18        targets = torch.FloatTensor(B, N,1).uniform_(min_val,max_val).to(device)
19    else:
20        targets = torch.FloatTensor(N,1).uniform_(min_val,max_val).to(device)
21    
22
23    targets =  -1*torch.pow(10,targets)
24    
25    return targets
26
27def generate_pressure_targets(N:int,B:int=1, max_val:float=5000, min_val:float=3000) -> Tensor:
28    '''
29    Generates a tensor of random pressure values\\
30    :param N: Number of values per batch
31    :param B: Number of batches to produce
32    :param max_val: Maximum value that can be generated. Default: `5000`
33    :param min_val: Minimum value that can be generated. Default: `3000`
34    Returns tensor of values
35    '''
36    targets = torch.FloatTensor(B, N,1).uniform_(min_val,max_val).to(device)
37    return targets
def generate_gorkov_targets( N: int, B: int = 1, max_val: float = -5, min_val: float = -5, log=True) -> torch.Tensor: 
 8def generate_gorkov_targets(N:int,B:int=1, max_val:float=-5, min_val:float=-5, log=True) -> Tensor:
 9    '''
10    Generates a tensor of random negative Gor'kov potential values\n
11    If `B=0` will return tensor with shape of `Nx1` else  will have shape `BxNx1`\n
12    :param N: Number of values per batch
13    :param B: Number of batches to produce
14    :param max_val: Maximum exponent of the value that can be generated. Default: `0`
15    :param min_val: Minimum exponent of the value that can be generated. Default: `-1e-4`
16    :return: tensor of values
17'''
18   
19    if B > 0:
20        targets = torch.FloatTensor(B, N,1).uniform_(min_val,max_val).to(device)
21    else:
22        targets = torch.FloatTensor(N,1).uniform_(min_val,max_val).to(device)
23    
24
25    targets =  -1*torch.pow(10,targets)
26    
27    return targets

Generates a tensor of random negative Gor'kov potential values

If B=0 will return tensor with shape of Nx1 else will have shape BxNx1

Parameters
  • N: Number of values per batch
  • B: Number of batches to produce
  • max_val: Maximum exponent of the value that can be generated. Default: 0
  • min_val: Minimum exponent of the value that can be generated. Default: -1e-4
Returns

tensor of values

def generate_pressure_targets( N: int, B: int = 1, max_val: float = 5000, min_val: float = 3000) -> torch.Tensor: 
29def generate_pressure_targets(N:int,B:int=1, max_val:float=5000, min_val:float=3000) -> Tensor:
30    '''
31    Generates a tensor of random pressure values\\
32    :param N: Number of values per batch
33    :param B: Number of batches to produce
34    :param max_val: Maximum value that can be generated. Default: `5000`
35    :param min_val: Minimum value that can be generated. Default: `3000`
36    Returns tensor of values
37    '''
38    targets = torch.FloatTensor(B, N,1).uniform_(min_val,max_val).to(device)
39    return targets

Generates a tensor of random pressure values\

Parameters
  • N: Number of values per batch
  • B: Number of batches to produce
  • max_val: Maximum value that can be generated. Default: 5000
  • min_val: Minimum value that can be generated. Default: 3000 Returns tensor of values