Docs (#81)

* clean `condition` module
* add docs

pina/pinn.py

@@ -26,21 +26,22 @@ class PINN(object):
                  device='cpu',
                  error_norm='mse'):
         '''
-        :param Problem problem: the formualation of the problem.
+        :param AbstractProblem problem: the formualation of the problem.
         :param torch.nn.Module model: the neural network model to use.
-        :param torch.optim optimizer: the neural network optimizer to use;
-            default is `torch.optim.Adam`.
+        :param torch.optim.Optimizer optimizer: the neural network optimizer to
+            use; default is `torch.optim.Adam`.
         :param dict optimizer_kwargs: Optimizer constructor keyword args.
         :param float lr: the learning rate; default is 0.001.
-        :param torch.optim.lr_scheduler._LRScheduler lr_scheduler_type: Learning rate scheduler.
+        :param torch.optim.LRScheduler lr_scheduler_type: Learning
+            rate scheduler.
         :param dict lr_scheduler_kwargs: LR scheduler constructor keyword args.
         :param float regularizer: the coefficient for L2 regularizer term.
         :param type dtype: the data type to use for the model. Valid option are
             `torch.float32` and `torch.float64` (`torch.float16` only on GPU);
             default is `torch.float64`.
-        :param string device: the device used for training; default 'cpu'
+        :param str device: the device used for training; default 'cpu'
             option include 'cuda' if cuda is available.
-        :param string/int error_norm: the loss function used as minimizer,
+        :param (str, int) error_norm: the loss function used as minimizer,
             default mean square error 'mse'. If string options include mean
             error 'me' and mean square error 'mse'. If int, the p-norm is
             calculated where p is specifined by the int input.
@@ -161,6 +162,9 @@ class PINN(object):
 
     def span_pts(self, *args, **kwargs):
         """
+        Generate a set of points to span the `Location` of all the conditions of
+        the problem.
+
         >>> pinn.span_pts(n=10, mode='grid')
         >>> pinn.span_pts(n=10, mode='grid', location=['bound1'])
         >>> pinn.span_pts(n=10, mode='grid', variables=['x'])