fixed astensor when taking Tensor or Result inputs

_test_base_tensor.py

@@ -5,14 +5,14 @@ from copy import copy
 from compile import Function
 import gof
 
-def _tensor(data, broadcastable=None, role=None, name=None):
+def _tensor(data, broadcastable=None, name=None):
     """Return a BaseTensor containing given data"""
     data = numpy.asarray(data)
     if broadcastable is None:
         broadcastable = [s==1 for s in data.shape]
     elif broadcastable in [0, 1]:
         broadcastable = [broadcastable] *  len(data.shape)
-    rval = BaseTensor(data.dtype, broadcastable, role, name)
+    rval = BaseTensor(data.dtype, broadcastable, name)
     rval.data = data # will raise if broadcastable was mis-specified
     return rval
 

base_tensor.py

@@ -26,7 +26,7 @@ class BaseTensor(Result):
     on the L{Op}s that use it.
     """
 
-    def __init__(self, dtype, broadcastable, role=None, name=None):
+    def __init__(self, dtype, broadcastable, name=None):
         """Initialize a L{Tensor}
 
         @todo: Initialize a L{Tensor} or a L{BaseTensor}? -jpt
@@ -45,7 +45,7 @@ class BaseTensor(Result):
         # the argument that is awkward to construct, I decided to put all this
         # into the tensor(data,...) function below, which is like a second
         # constructor that works with an ndarray.
-        Result.__init__(self, role=role, name=name)
+        Result.__init__(self, role=None, name=name)
         self._dtype = str(dtype)
         self.dtype_specs() # this is just for error checking
         self._broadcastable = tuple(broadcastable)
@@ -232,7 +232,7 @@ class BaseTensor(Result):
         If transfer_data is True, a copy of self.data is assigned to the copy's
         data property, otherwise the copy's data is left as None.
         """
-        cpy = self.__class__(self.dtype, self.broadcastable, None, self.name)
+        cpy = self.__class__(self.dtype, self.broadcastable, self.name)
         if transfer_data:
             cpy.data = copy(self.data)
         return cpy

tensor.py

@@ -75,16 +75,27 @@ s2t.Tensor = Tensor
 
 
 # alternate Tensor constructor
-def astensor(data, broadcastable=None, role=None, name=None):
+def astensor(data, broadcastable=None, name=None):
     """Return a L{Tensor} containing given data"""
-    if isinstance(data, Tensor) and broadcastable is None and role is None and name is None:
-        return data
+    if isinstance(data, BaseTensor):
+        if broadcastable is not None and list(data.broadcastable) != list(broadcastable):
+            raise TypeError("The data to wrap as a Tensor has the wrong broadcastable pattern. Expected %s, got %s." % (broadcastable, data.broadcastable))
+        if isinstance(data, Tensor) and (name is None or name == data.name):
+            return data
+        else:
+            return Tensor(data.dtype, data.broadcastable, name = name)
+    elif isinstance(data, Result):
+        data = data.data
+        
+    if data is None and broadcastable is None:
+        raise TypeError("Cannot make a Tensor out of None or a Result with no data.")
+    
     data = numpy.asarray(data)
     if broadcastable is None:
         broadcastable = [s==1 for s in data.shape]
     elif broadcastable in [0, 1]:
         broadcastable = [broadcastable] *  len(data.shape)
-    rval = Tensor(data.dtype, broadcastable, role, name)
+    rval = Tensor(data.dtype, broadcastable, name = name)
     rval.data = data # will raise if broadcastable was mis-specified
     return rval
 s2t.astensor = astensor