Minor cleanup (#45)

* cleanup
* codacy
* move merge_tensors to utils.py

pina/pinn.py

@@ -3,6 +3,7 @@ import torch
 
 from .problem import AbstractProblem
 from .label_tensor import LabelTensor
+from .utils import merge_tensors
 
 torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732
 
@@ -126,27 +127,6 @@ class PINN(object):
         >>> pinn.span_pts(n=10, mode='grid', location=['bound1'])
         >>> pinn.span_pts(n=10, mode='grid', variables=['x'])
         """
-
-        def merge_tensors(tensors):  # name to be changed
-            if len(tensors) == 2:
-                tensor1 = tensors[0]
-                tensor2 = tensors[1]
-                n1 = tensor1.shape[0]
-                n2 = tensor2.shape[0]
-
-                tensor1 = LabelTensor(
-                    tensor1.repeat(n2, 1),
-                    labels=tensor1.labels)
-                tensor2 = LabelTensor(
-                    tensor2.repeat_interleave(n1, dim=0),
-                    labels=tensor2.labels)
-                return tensor1.append(tensor2)
-            elif len(tensors) == 1:
-                return tensors[0]
-            else:
-                recursive_result = merge_tensors(tensors[1:])
-                return merge_tensors([tensors[0], recursive_result])
-
         if isinstance(args[0], int) and isinstance(args[1], str):
             argument = {}
             argument['n'] = int(args[0])
@@ -171,19 +151,20 @@ class PINN(object):
         for location in locations:
             condition = self.problem.conditions[location]
 
-            pts = merge_tensors([
+            samples = tuple(condition.location.sample(
-                condition.location.sample(
                             argument['n'],
                             argument['mode'],
                             variables=argument['variables'])
-                for argument in arguments])
+                        for argument in arguments)
+            pts = merge_tensors(samples)
 
-            self.input_pts[location] = pts  #.double()  # TODO
+            # TODO
-            self.input_pts[location] = (
+            # pts = pts.double()
-                self.input_pts[location].to(dtype=self.dtype,
+            pts = pts.to(dtype=self.dtype, device=self.device)
-                                            device=self.device))
+            pts.requires_grad_(True)
-            self.input_pts[location].requires_grad_(True)
+            pts.retain_grad()
-            self.input_pts[location].retain_grad()
+
+            self.input_pts[location] = pts
 
     def train(self, stop=100, frequency_print=2, save_loss=1, trial=None):
 
@@ -193,17 +174,14 @@ class PINN(object):
         for condition_name in self.problem.conditions:
             condition = self.problem.conditions[condition_name]
 
-            if hasattr(condition, 'function'):
+            if (hasattr(condition, 'function') and
-                if isinstance(condition.function, list):
+                    isinstance(condition.function, list)):
                 for function in condition.function:
                     header.append(f'{condition_name}{function.__name__}')
-
+            else:
-                    continue
-
                 header.append(f'{condition_name}')
 
         while True:
-
             losses = []
 
             for condition_name in self.problem.conditions:

pina/utils.py

@@ -1,4 +1,6 @@
 """Utils module"""
+from functools import reduce
+
 
 def number_parameters(model, aggregate=True, only_trainable=True): #TODO: check
     """
@@ -25,3 +27,19 @@ def number_parameters(model, aggregate=True, only_trainable=True): #TODO: check
         tmp = sum(tmp.values())
 
     return tmp
+
+
+def merge_tensors(tensors):  # name to be changed
+    if tensors:
+        return reduce(merge_two_tensors, tensors[1:], tensors[0])
+    raise ValueError("Expected at least one tensor")
+
+
+def merge_two_tensors(tensor1, tensor2):
+    n1 = tensor1.shape[0]
+    n2 = tensor2.shape[0]
+
+    tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
+    tensor2 = LabelTensor(tensor2.repeat_interleave(n1, dim=0),
+                            labels=tensor2.labels)
+    return tensor1.append(tensor2)