Codacy Small Bug Fixes:

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

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)