solving span bugs (#57)

pina/pinn.py

@@ -173,7 +173,16 @@ class PINN(object):
         >>> pinn.span_pts(n=10, mode='grid', location=['bound1'])
         >>> pinn.span_pts(n=10, mode='grid', variables=['x'])
         """
-        if isinstance(args[0], int) and isinstance(args[1], str):
+
+        if all(key in kwargs for key in ['n', 'mode']):
+            argument = {}
+            argument['n'] = kwargs['n']
+            argument['mode'] = kwargs['mode']
+            argument['variables'] = self.problem.input_variables
+            arguments = [argument]
+        elif any(key in kwargs for key in ['n', 'mode']) and args:
+            raise ValueError("Don't mix args and kwargs")
+        elif isinstance(args[0], int) and isinstance(args[1], str):
             argument = {}
             argument['n'] = int(args[0])
             argument['mode'] = args[1]
@@ -181,12 +190,6 @@ class PINN(object):
             arguments = [argument]
         elif all(isinstance(arg, dict) for arg in args):
             arguments = args
-        elif all(key in kwargs for key in ['n', 'mode']):
-            argument = {}
-            argument['n'] = kwargs['n']
-            argument['mode'] = kwargs['mode']
-            argument['variables'] = self.problem.input_variables
-            arguments = [argument]
         else:
             raise RuntimeError
 

pina/span.py

@@ -100,6 +100,25 @@ class Span(Location):
                     result = result.append(pts_variable, mode='std')
             return result
 
+        def _single_points_sample(n, variables):
+            tmp = []
+            for variable in variables:
+                if variable in self.fixed_.keys():
+                    value = self.fixed_[variable]
+                    pts_variable = torch.tensor([[value]]).repeat(n, 1)
+                    pts_variable = pts_variable.as_subclass(LabelTensor)
+                    pts_variable.labels = [variable]
+                    tmp.append(pts_variable)
+
+            result = tmp[0]
+            for i in tmp[1:]:
+                result = result.append(i, mode='std')
+
+            return result
+
+        if self.fixed_ and (not self.range_):
+            return _single_points_sample(n, variables)
+
         if variables == 'all':
             variables = list(self.range_.keys()) + list(self.fixed_.keys())
 

tests/test_pinn.py

@@ -70,6 +70,32 @@ def test_span_pts():
     assert pinn.input_pts['D'].shape[0] == n
 
 
+def test_sampling_all_args():
+    pinn = PINN(problem, model)
+    n = 10
+    pinn.span_pts(n, 'grid', locations=['D'])
+
+
+def test_sampling_all_kwargs():
+    pinn = PINN(problem, model)
+    n = 10
+    pinn.span_pts(n=n, mode='latin', locations=['D'])
+
+
+def test_sampling_dict():
+    pinn = PINN(problem, model)
+    n = 10
+    pinn.span_pts(
+        {'variables': ['x', 'y'], 'mode': 'grid', 'n': n}, locations=['D'])
+
+
+def test_sampling_mixed_args_kwargs():
+    pinn = PINN(problem, model)
+    n = 10
+    with pytest.raises(ValueError):
+        pinn.span_pts(n, mode='latin', locations=['D'])
+
+
 def test_train():
     pinn = PINN(problem, model)
     boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
@@ -130,4 +156,4 @@ if torch.cuda.is_available():
         n = 100
         pinn.span_pts(n, 'grid', boundaries)
         pinn.span_pts(n, 'grid', locations=['D'])
-        pinn.train(5)
+        pinn.train(5)