Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

pina/model/block/utils_convolution.py

@@ -0,0 +1,49 @@
+import torch
+
+
+def check_point(x, current_stride, dim):
+    max_stride = current_stride + dim
+    indeces = torch.logical_and(
+        x[..., :-1] < max_stride, x[..., :-1] >= current_stride
+    ).all(dim=-1)
+    return indeces
+
+
+def map_points_(x, filter_position):
+    """Mapping function n dimensional case
+
+    :param x: input data of two dimension
+    :type x: torch.tensor
+    :param filter_position: position of the filter
+    :type dim: list[numeric]
+    :return: data mapped inplace
+    :rtype: torch.tensor
+    """
+    x.add_(-filter_position)
+
+    return x
+
+
+def optimizing(f):
+    """Decorator for calling a function just once
+
+    :param f: python function
+    :type f: function
+    """
+
+    def wrapper(*args, **kwargs):
+
+        if kwargs["type"] == "forward":
+            if not wrapper.has_run_inverse:
+                wrapper.has_run_inverse = True
+                return f(*args, **kwargs)
+
+        if kwargs["type"] == "inverse":
+            if not wrapper.has_run:
+                wrapper.has_run = True
+                return f(*args, **kwargs)
+
+    wrapper.has_run_inverse = False
+    wrapper.has_run = False
+
+    return wrapper