Codacy Small Bug Fixes:

- cleaned up imports
- cleaned up some code
- added docstrings

examples/problems/parametric_elliptic_optimal_control.py

@@ -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...')

examples/problems/poisson.py

@@ -1,3 +1,4 @@
+""" Poisson equation example. """
 import numpy as np
 import torch
 
@@ -49,5 +50,6 @@ class Poisson(SpatialProblem):
             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

examples/run_burgers.py

@@ -1,3 +1,4 @@
+"""Run PINA on Burgers equation"""
 import argparse
 import torch
 from torch.nn import Softplus
@@ -11,6 +12,7 @@ class myFeature(torch.nn.Module):
     """
     Feature: sin(pi*x)
     """
+
     def __init__(self, idx):
         super(myFeature, self).__init__()
         self.idx = idx

examples/run_parametric_elliptic_optimal_control_alpha.py

@@ -1,84 +1,87 @@
-# import argparse
-# import numpy as np
-# import torch
-# from torch.nn import Softplus
+import argparse
+import numpy as np
+import torch
+from torch.nn import Softplus
 
-# from pina import PINN, LabelTensor, Plotter
-# from pina.model import MultiFeedForward
-# from problems.parametric_elliptic_optimal_control_alpha_variable import (
-#     ParametricEllipticOptimalControl)
+from pina import PINN, LabelTensor, Plotter
+from pina.model import MultiFeedForward
+from problems.parametric_elliptic_optimal_control_alpha_variable import (
+    ParametricEllipticOptimalControl)
 
 
-# class myFeature(torch.nn.Module):
-#     """
-#     Feature: sin(x)
-#     """
+class myFeature(torch.nn.Module):
+    """
+    Feature: sin(x)
+    """
 
-#     def __init__(self):
-#         super(myFeature, self).__init__()
+    def __init__(self):
+        super(myFeature, self).__init__()
 
-#     def forward(self, x):
-#         t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1)
-#         return LabelTensor(t, ['k0'])
+    def forward(self, x):
+        t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1)
+        return LabelTensor(t, ['k0'])
 
 
-# class CustomMultiDFF(MultiFeedForward):
+class CustomMultiDFF(MultiFeedForward):
 
-#     def __init__(self, dff_dict):
-#         super().__init__(dff_dict)
+    def __init__(self, dff_dict):
+        super().__init__(dff_dict)
 
-#     def forward(self, x):
-#         out = self.uu(x)
-#         p = LabelTensor((out.extract(['u_param']) * x.extract(['alpha'])), ['p'])
-#         return out.append(p)
+    def forward(self, x):
+        out = self.uu(x)
+        p = LabelTensor(
+            (out.extract(['u_param']) * x.extract(['alpha'])), ['p'])
+        return out.append(p)
 
 
-# if __name__ == "__main__":
+if __name__ == "__main__":
 
-#     parser = argparse.ArgumentParser(description="Run PINA")
-#     group = parser.add_mutually_exclusive_group(required=True)
-#     group.add_argument("-s", "-save", action="store_true")
-#     group.add_argument("-l", "-load", action="store_true")
-#     args = parser.parse_args()
+    parser = argparse.ArgumentParser(description="Run PINA")
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("-s", "-save", action="store_true")
+    group.add_argument("-l", "-load", action="store_true")
+    args = parser.parse_args()
 
-#     opc = ParametricEllipticOptimalControl()
-#     model = CustomMultiDFF(
-#         {
-#             'uu': {
-#                 'input_variables': ['x1', 'x2', 'mu', 'alpha'],
-#                 'output_variables': ['u_param', 'y'],
-#                 'layers': [40, 40, 20],
-#                 'func': Softplus,
-#                 'extra_features': [myFeature()],
-#             },
-#         }
-#     )
+    opc = ParametricEllipticOptimalControl()
+    model = CustomMultiDFF(
+        {
+            'uu': {
+                'input_variables': ['x1', 'x2', 'mu', 'alpha'],
+                'output_variables': ['u_param', 'y'],
+                'layers': [40, 40, 20],
+                'func': Softplus,
+                'extra_features': [myFeature()],
+            },
+        }
+    )
 
-#     pinn = PINN(
-#         opc,
-#         model,
-#         lr=0.002,
-#         error_norm='mse',
-#         regularizer=1e-8)
+    pinn = PINN(
+        opc,
+        model,
+        lr=0.002,
+        error_norm='mse',
+        regularizer=1e-8)
 
-#     if args.s:
+    if args.s:
 
-#         pinn.span_pts(
-#             {'variables': ['x1', 'x2'], 'mode': 'random', 'n': 100},
-#             {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
-#             locations=['D'])
-#         pinn.span_pts(
-#             {'variables': ['x1', 'x2'], 'mode': 'grid', 'n': 20},
-#             {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
-#             locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
+        pinn.span_pts(
+            {'variables': ['x1', 'x2'], 'mode': 'random', 'n': 100},
+            {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
+            locations=['D'])
+        pinn.span_pts(
+            {'variables': ['x1', 'x2'], 'mode': 'grid', 'n': 20},
+            {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
+            locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
 
-#         pinn.train(1000, 20)
-#         pinn.save_state('pina.ocp')
+        pinn.train(1000, 20)
+        pinn.save_state('pina.ocp')
 
-#     else:
-#         pinn.load_state('pina.ocp')
-#         plotter = Plotter()
-#         plotter.plot(pinn, components='y', fixed_variables={'alpha': 0.01, 'mu': 1.0})
-#         plotter.plot(pinn, components='u_param', fixed_variables={'alpha': 0.01, 'mu': 1.0})
-#         plotter.plot(pinn, components='p', fixed_variables={'alpha': 0.01, 'mu': 1.0})
-raise NotImplementedError('not available problem at the moment...')
+    else:
+        pinn.load_state('pina.ocp')
+        plotter = Plotter()
+        plotter.plot(pinn, components='y',
+                     fixed_variables={'alpha': 0.01, 'mu': 1.0})
+        plotter.plot(pinn, components='u_param',
+                     fixed_variables={'alpha': 0.01, 'mu': 1.0})
+        plotter.plot(pinn, components='p', fixed_variables={
+                     'alpha': 0.01, 'mu': 1.0})

examples/run_poisson_deeponet.py

@@ -48,7 +48,10 @@ class myRBF(torch.nn.Module):
         result = self.a * torch.exp(-(x - self.b)**2/(self.c**2))
         return result
 
+
 class myModel(torch.nn.Module):
+    """ Model for the Poisson equation."""
+
     def __init__(self):
 
         super().__init__()
@@ -60,6 +63,7 @@ class myModel(torch.nn.Module):
         result.labels = ['u']
         return result
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run PINA")
     parser.add_argument("-s", "--save", action="store_true")
@@ -97,7 +101,8 @@ if __name__ == "__main__":
             print(model.ffn_x.b)
             print(model.ffn_x.c)
 
-            xi = torch.linspace(0, 1, 64).reshape(-1, 1).as_subclass(LabelTensor)
+            xi = torch.linspace(0, 1, 64).reshape(-1,
+                                                  1).as_subclass(LabelTensor)
             xi.labels = ['x']
             yi = model.ffn_x(xi)
             y_truth = -torch.sin(xi*torch.pi)

examples/run_stokes.py

@@ -1,10 +1,9 @@
 import argparse
-import sys
 import numpy as np
 import torch
 from torch.nn import ReLU, Tanh, Softplus
 
-from pina import PINN, LabelTensor, Plotter
+from pina import PINN, Plotter
 from pina.model import FeedForward
 from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
 from problems.stokes import Stokes

pina/adaptive_functions/adaptive_linear.py

@@ -1,3 +1,4 @@
+""" Implementation of adaptive linear layer. """
 import torch
 from torch.nn.parameter import Parameter
 

pina/adaptive_functions/adaptive_relu.py

@@ -1,7 +1,7 @@
 import torch
 from torch.nn.parameter import Parameter
 
-class AdaptiveReLU(torch.nn.Module):
+class AdaptiveReLU(torch.nn.Module, Parameter):
     '''
     Implementation of soft exponential activation.
     Shape:

pina/dataset.py

@@ -1,4 +1,3 @@
-""" """
 from torch.utils.data import Dataset, DataLoader
 import functools
 

pina/geometry/cartesian.py

@@ -82,7 +82,8 @@ class CartesianDomain(Location):
             pts = chebyshev_roots(n).mul(.5).add(.5).reshape(-1, 1)
         elif mode == 'grid':
             pts = torch.linspace(0, 1, n).reshape(-1, 1)
-        elif mode == 'lh' or mode == 'latin':
+        # elif mode == 'lh' or mode == 'latin':
+        elif mode in ['lh', 'latin']:
             pts = torch_lhs(n, dim)
 
         pts *= bounds[:, 1] - bounds[:, 0]

pina/model/layers/integral.py

@@ -1,3 +1,4 @@
+""" Integral class for continous convolution"""
 import torch
 
 

pina/model/network.py

@@ -4,6 +4,8 @@ from ..utils import check_consistency
 
 
 class Network(torch.nn.Module):
+    """ Network class with starndard forward method 
+    and possibility to pass extra features."""
 
     def __init__(self, model, extra_features=None):
         super().__init__()

pina/plotter.py

@@ -1,6 +1,5 @@
 """ Module for plotting. """
 import matplotlib.pyplot as plt
-import numpy as np
 import torch
 
 from pina import LabelTensor
@@ -43,7 +42,8 @@ class Plotter:
         proj = '3d' if len(variables) == 3 else None
         ax = fig.add_subplot(projection=proj)
         for location in solver.problem.input_pts:
-            coords = solver.problem.input_pts[location].extract(variables).T.detach()
+            coords = solver.problem.input_pts[location].extract(
+                variables).T.detach()
             if coords.shape[0] == 1:  # 1D samples
                 ax.plot(coords[0], torch.zeros(coords[0].shape), '.',
                         label=location)