Fixed docstrings.

fluents/dataset.py

@@ -36,6 +36,7 @@ class Dataset:
 
     data = Dataset(rand(10,20)) (generates dims and ids (no links))
     """
+    
     def __init__(self, array, identifiers=None, name='Unnamed dataset'):
         self._dims = [] #existing dimensions in this dataset
         self._map = {} # internal mapping for dataset:  identifier <--> index
@@ -195,6 +196,20 @@ class Dataset:
         return asarray(index)        
 
     def existing_identifiers(self, dim, idents):
+        """Filters a list of identifiers to find those that are present in the
+        dataset.
+
+        The most common use of this function is to get a list of
+        identifiers who correspond one to one with the list of indices produced
+        when get_indices is given an identifier list. That is
+        ds.get_indices(dim, idents) and ds.exisiting_identifiers(dim, idents)
+        will have the same order.
+
+        @param dim: A dimension present in the dataset.
+        @param idents: A list of identifiers along the given dimension.
+        @return: A list of identifiers in the same order as idents, but
+        without elements not present in the dataset.
+        """
         if not isinstance(idents, list) and not isinstance(idents, set):
             raise ValueError("idents needs to be a list/set got: %s" %type(idents))
 
@@ -410,6 +425,12 @@ class Selection(dict):
     
 
 def write_ftsv(fd, ds):
+    """Writes a dataset in fluents tab separated values (ftsv) form.
+    
+    @param fd: An open file descriptor to the output file.
+    @param ds: The dataset to be written. The function handles datasets
+    of these classes: Dataset, CategoryDataset and GraphDataset
+    """
     # Write header information
     if isinstance(ds, CategoryDataset):
         type = 'category'
@@ -443,6 +464,12 @@ def write_ftsv(fd, ds):
 
 
 def read_ftsv(fd):
+    """Read a dataset in fluents tab separated values (ftsv) form and return it.
+    
+    @param fd: An open file descriptor.
+    @return: A Dataset, CategoryDataset or GraphDataset depending on the information
+    read.
+    """
     split_re = re.compile('^#\s*(\w+)\s*:\s*(.+)')
     dimensions = []
     identifiers = {}