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Codacy Small Bug Fixes:

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- cleaned up imports
- cleaned up some code
- added docstrings
This commit is contained in:
SpartaKushK
2023-07-25 16:43:45 +02:00
committed by Nicola Demo
parent bd88e24174
commit 625a77c0d5
13 changed files with 132 additions and 118 deletions
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83
examples/problems/parametric_elliptic_optimal_control.py
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@@ -1,53 +1,52 @@
# import numpy as np
# import torch
# from pina.problem import Problem
# from pina.segment import Segment
# from pina.cube import Cube
# from pina.problem2d import Problem2D
import numpy as np
import torch
from pina.segment import Segment
from pina.cube import Cube
from pina.problem2d import Problem2D
# xmin, xmax, ymin, ymax = -1, 1, -1, 1
xmin, xmax, ymin, ymax = -1, 1, -1, 1
# class ParametricEllipticOptimalControl(Problem2D):
class ParametricEllipticOptimalControl(Problem2D):
# def __init__(self, alpha=1):
def __init__(self, alpha=1):
# def term1(input_, param_, output_):
# grad_p = self.grad(output_['p'], input_)
# gradgrad_p_x1 = self.grad(grad_p['x1'], input_)
# gradgrad_p_x2 = self.grad(grad_p['x2'], input_)
# return output_['y'] - param_ - (gradgrad_p_x1['x1'] + gradgrad_p_x2['x2'])
def term1(input_, param_, output_):
grad_p = self.grad(output_['p'], input_)
gradgrad_p_x1 = self.grad(grad_p['x1'], input_)
gradgrad_p_x2 = self.grad(grad_p['x2'], input_)
return output_['y'] - param_ - (gradgrad_p_x1['x1'] + gradgrad_p_x2['x2'])
# def term2(input_, param_, output_):
# grad_y = self.grad(output_['y'], input_)
# gradgrad_y_x1 = self.grad(grad_y['x1'], input_)
# gradgrad_y_x2 = self.grad(grad_y['x2'], input_)
# return - (gradgrad_y_x1['x1'] + gradgrad_y_x2['x2']) - output_['u_param']
def term2(input_, param_, output_):
grad_y = self.grad(output_['y'], input_)
gradgrad_y_x1 = self.grad(grad_y['x1'], input_)
gradgrad_y_x2 = self.grad(grad_y['x2'], input_)
return - (gradgrad_y_x1['x1'] + gradgrad_y_x2['x2']) - output_['u_param']
# def term3(input_, param_, output_):
# return output_['p'] - output_['u_param']*alpha
def term3(input_, param_, output_):
return output_['p'] - output_['u_param']*alpha
# def term(input_, param_, output_):
# return term1( input_, param_, output_) +term2( input_, param_, output_) + term3( input_, param_, output_)
def term(input_, param_, output_):
return term1( input_, param_, output_) +term2( input_, param_, output_) + term3( input_, param_, output_)
# def nil_dirichlet(input_, param_, output_):
# y_value = 0.0
# p_value = 0.0
# return torch.abs(output_['y'] - y_value) + torch.abs(output_['p'] - p_value)
def nil_dirichlet(input_, param_, output_):
y_value = 0.0
p_value = 0.0
return torch.abs(output_['y'] - y_value) + torch.abs(output_['p'] - p_value)
# self.conditions = {
# 'gamma1': {'location': Segment((xmin, ymin), (xmax, ymin)), 'func': nil_dirichlet},
# 'gamma2': {'location': Segment((xmax, ymin), (xmax, ymax)), 'func': nil_dirichlet},
# 'gamma3': {'location': Segment((xmax, ymax), (xmin, ymax)), 'func': nil_dirichlet},
# 'gamma4': {'location': Segment((xmin, ymax), (xmin, ymin)), 'func': nil_dirichlet},
# 'D1': {'location': Cube([[xmin, xmax], [ymin, ymax]]), 'func': term},
# #'D2': {'location': Cube([[0, 1], [0, 1]]), 'func': term2},
# #'D3': {'location': Cube([[0, 1], [0, 1]]), 'func': term3}
# }
self.conditions = {
'gamma1': {'location': Segment((xmin, ymin), (xmax, ymin)), 'func': nil_dirichlet},
'gamma2': {'location': Segment((xmax, ymin), (xmax, ymax)), 'func': nil_dirichlet},
'gamma3': {'location': Segment((xmax, ymax), (xmin, ymax)), 'func': nil_dirichlet},
'gamma4': {'location': Segment((xmin, ymax), (xmin, ymin)), 'func': nil_dirichlet},
'D1': {'location': Cube([[xmin, xmax], [ymin, ymax]]), 'func': term},
#'D2': {'location': Cube([[0, 1], [0, 1]]), 'func': term2},
#'D3': {'location': Cube([[0, 1], [0, 1]]), 'func': term3}
}
self.input_variables = ['x1', 'x2']
self.output_variables = ['u', 'p', 'y']
self.parameters = ['mu']
self.spatial_domain = Cube([[xmin, xmax], [xmin, xmax]])
self.parameter_domain = np.array([[0.5, 3]])
# self.input_variables = ['x1', 'x2']
# self.output_variables = ['u', 'p', 'y']
# self.parameters = ['mu']
# self.spatial_domain = Cube([[xmin, xmax], [xmin, xmax]])
# self.parameter_domain = np.array([[0.5, 3]])
raise NotImplementedError('not available problem at the moment...')

4
examples/problems/poisson.py
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@@ -1,3 +1,4 @@
""" Poisson equation example. """
import numpy as np
import torch
@@ -46,8 +47,9 @@ class Poisson(SpatialProblem):
# real poisson solution
def poisson_sol(self, pts):
return -(
torch.sin(pts.extract(['x'])*torch.pi)*
torch.sin(pts.extract(['x'])*torch.pi) *
torch.sin(pts.extract(['y'])*torch.pi)
)/(2*torch.pi**2)
# return -(np.sin(x*np.pi)*np.sin(y*np.pi))/(2*np.pi**2)
truth_solution = poisson_sol