merged

compile.py

@@ -210,12 +210,33 @@ class _test_single(unittest.TestCase):
         core.pop_mode()
 
     def test_3(self):
-        a = core.Numpy2(data=numpy.ones((2,2)))
-        b = core.Numpy2(data=numpy.ones((2,2)))
+        a = core.Numpy2(data=numpy.random.rand(2,2))
+        b = core.Numpy2(data=numpy.random.rand(2,2))
         c = core.add(a,b)
+
+        self.failUnless(c.data is None)
+        self.failUnless(c.state is Empty)
+
+        new_a = numpy.random.rand(2,2)
+        new_b = numpy.random.rand(2,2)
+        a.data = new_a
+        b.data = new_b
         p = single(c)
         p()
-        self.failUnless(core._approx_eq(c, numpy.ones((2,2))*2))
+        self.failUnless(core._approx_eq(c, new_a + new_b))
+
+    def test_get_element(self):
+        a_data = numpy.random.rand(2,2)
+        a = core.Numpy2(data=a_data)
+        a_i = a[0,0]
+        p = single(a_i)
+
+        for i in 0,1:
+            for j in 0,1:
+                p()
+                self.failUnless(a_data[i,j] == a_i.data)
+
+
 
 if __name__ == '__main__':
     unittest.main()

core.py

@@ -116,33 +116,33 @@ class Numpy2(ResultBase):
     ################################
     # Numpy2 specific functionality
     #
-    __array__ = property(lambda self: self._data.__array__ )
-    __array_struct__ = property(lambda self: self._data.__array_struct__ )
+    __array__ = property(lambda self: self.data.__array__ )
+    __array_struct__ = property(lambda self: self.data.__array_struct__ )
 
     def data_alloc(self):
         return numpy.ndarray(self.shape, self.dtype)
 
-    # self._dtype is used when self._data hasn't been set yet
+    # self._dtype is used when self.data hasn't been set yet
     def __dtype_get(self):
-        if self._data is None:
+        if self.data is None:
             return self._dtype
         else:
-            return self._data.dtype
+            return self.data.dtype
     def __dtype_set(self, dtype):
-        if self._data is None:
+        if self.data is None:
             self._dtype = dtype
         else:
             raise StateError('cannot set dtype after data has been set')
     dtype = property(__dtype_get, __dtype_set)
 
-    # self._shape is used when self._data hasn't been set yet
+    # self._shape is used when self.data hasn't been set yet
     def __shape_get(self):
-        if self._data is None:
+        if self.data is None:
             return self._shape
         else:
-            return self._data.shape
+            return self.data.shape
     def __shape_set(self, shape):
-        if self._data is None:
+        if self.data is None:
             self._shape = shape
         else:
             raise StateError('cannot set shape after data has been set')
@@ -1729,7 +1729,7 @@ class _testCase_slicing(unittest.TestCase):
         wa1 = wrap(a)[0:8:2]
         for i in xrange(8): a[i] = i
 
-        self.failUnless(wa1.data.shape == (4,))
+        self.failUnless(wa1.shape == (4,))
         for i in xrange(4):
             self.failUnless(a[i*2] == wa1.data[i])
     def test_getslice_3d_float(self):
@@ -1737,10 +1737,18 @@ class _testCase_slicing(unittest.TestCase):
         a = numpy.asarray(range(4*5*6))
         a.resize((4,5,6))
         wa1 = wrap(a)[1:3]
-        wa1.data.shape
+        self.failUnless(wa1.shape == (2,5,6))
         self.failUnless(numpy.all(a[1:3] == wa1.data))
         a[1] *= -1.0
         self.failUnless(numpy.all(a[1:3] == wa1.data))
+    def test_getslice_3d_one(self):
+        """Test getslice on 3d array"""
+        a = numpy.asarray(range(4*5*6))
+        a.resize((4,5,6))
+        wa = wrap(a)
+        wa_123 = wa[1,2,3]
+        self.failUnless(wa_123.shape == (), wa_123.shape)
+
 
 add = scalar_switch(add_elemwise, add_scalar, add_scalar)
 add_inplace = scalar_switch(add_elemwise_inplace, add_scalar_inplace)

gof/env.py

@@ -53,7 +53,22 @@ class InconsistencyError(GofError):
     """
     pass
 
+def require_set(cls):
+    """Return the set of objects named in a __env_require__ field in a base class"""
+    r = set()
 
+    if hasattr(cls, '__class__'):
+        cls = cls.__class__
+
+    bases = utils.all_bases(cls, lambda cls: hasattr(cls, '__env_require__'))
+
+    for base in bases:
+        req = base.__env_require__
+        if isinstance(req, (list, tuple)):
+            r.update(req)
+        else:
+            r.add(req)
+    return r
 
 class Env(graph.Graph):
     """
@@ -179,7 +194,7 @@ class Env(graph.Graph):
         return True
 
     def satisfy(self, x):
-        for feature_class in x.require():
+        for feature_class in require_set(x):
             self.add_feature(feature_class)
 
     def add_feature(self, feature_class, do_import = True):

gof/lib.py

+from copy import copy
 
 from op import Op
 from result import is_result, ResultBase
@@ -57,27 +58,31 @@ def compute(*nodes):
     """Recursively evaluate each node (in a quick & dirty way)."""
     compute_from(nodes, set())
 
-class ForbidConstantOverwrite(features.Listener, features.Constraint):
-
-    def __init__(self, env):
-        self.env = env
-        self.bad = set()
-
-    def root_inputs(self, input):
-        owner = input.owner
+def root_inputs(input):
+    """Return the leaves of a search through consecutive view_map()s"""
+    owner = input.owner
+    if owner:
         view_map = owner.view_map()
         if input in view_map:
             answer = []
             for input2 in view_map[input]:
-                answer += owner.root_inputs(input2)
+                answer.extend(root_inputs(input2))
             return answer
         else:
             return [input]
+    else:
+        return [input]
+
+class ForbidConstantOverwrite(features.Listener, features.Constraint):
+
+    def __init__(self, env):
+        self.env = env
+        self.bad = set()
 
     def on_import(self, op):
         for output, inputs in op.destroy_map().items():
             for input in inputs:
-                for root_input in self.root_inputs(input):
+                for root_input in root_inputs(input):
                     if getattr(root_input, 'constant', False):
                         self.bad.add(op)
                         return
@@ -185,9 +190,7 @@ class DestroyHandler(features.Listener, features.Constraint, features.Orderings)
             self.__detect_cycles_helper__(user, [])
 
     def get_maps(self, op):
-        vmap = getattr(op, 'view_map',{})
-        dmap = getattr(op, 'destoy_map', {})
-        return vmap, dmap
+        return op.view_map(), op.destroy_map()
 
     def on_import(self, op):
         view_map, destroy_map = self.get_maps(op)
@@ -347,6 +350,8 @@ class DestroyHandler(features.Listener, features.Constraint, features.Orderings)
 
 class NewPythonOp(Op):
 
+    __env_require__ = DestroyHandler
+
     def view_map(self):
         return {}
 
@@ -358,8 +363,6 @@ class PythonOp(NewPythonOp):
     
     __metaclass__ = ClsInit
 
-    __require__ = DestroyHandler
-    
     nout = 1
 
     @staticmethod
@@ -389,45 +392,28 @@ class PythonOp(NewPythonOp):
         NewPythonOp.__init__(self, inputs, self.gen_outputs())
 
     def __validate__(self):
-        return all([ is_result(i) for i in self.inputs])
+        return all([is_result(i) for i in self.inputs])
     
     def gen_outputs(self):
         raise AbstractFunctionError()
-    
-    @staticmethod
-    def root_inputs(input):
-        owner = input.owner
-        if owner:
-            view_map = owner.view_map()
-            if input in view_map:
-                answer = []
-                for input2 in view_map[input]:
-                    answer += owner.root_inputs(input2)
-                return answer
-            else:
-                return [input]
-        else:
-            return [input]
-
-    def input_is_up_to_date(self, input):
-        answer = True
-        for input in self.root_inputs(input):
-            answer &= input.up_to_date
-        return answer
-
-    def input_is_constant(self, input):
-        answer = False
-        for input in self.root_inputs(input):
-            answer |= input.constant
-        return answer
 
     def check_input(self, input):
+        def input_is_up_to_date(input):
+            answer = True
+            for input in root_inputs(input):
+                answer &= input.up_to_date
+            return answer
         if input.data is None:
             raise ValueError("Uncomputed input: %s in %s" % (input, self))
-        if not self.input_is_up_to_date(input):
+        if not input_is_up_to_date(input):
             raise ValueError("Input is out of date: %s in %s" % (input, self))
 
     def perform(self):
+        def input_is_constant(input):
+            answer = False
+            for input in root_inputs(input):
+                answer |= input.constant
+            return answer
         exc = set()
         for output, inputs in self.destroy_map().items():
             exc.update(inputs)
@@ -518,7 +504,7 @@ class PythonOp(NewPythonOp):
             if output not in except_list:
                 output.alloc()
     
-    __require__ = ForbidConstantOverwrite
+    __env_require__ = ForbidConstantOverwrite
 
     def __copy__(self):
         """
@@ -580,7 +566,7 @@ class PythonOpt(opt.Optimizer):
 
 
 
-class DummyOp(Op):
+class DummyOp(NewPythonOp):
     
     def __init__(self, input):
         Op.__init__(self, [input], [ResultBase()])

gof/op.py

@@ -28,7 +28,6 @@ class Op(object):
     """
 
     __slots__ = ['_inputs', '_outputs']
-    __require__ = []
 
     #create inputs and outputs as read-only attributes
     inputs = property(lambda self: self._inputs, doc = "The list of this Op's input Results.")
@@ -229,11 +228,10 @@ class Op(object):
         """
         r = set()
 
-        bases = all_bases(cls, lambda cls: hasattr(cls, '__require__'))
-        bases.append(cls)
+        bases = all_bases(cls, lambda cls: hasattr(cls, '__env_require__'))
 
         for base in bases:
-            req = base.__require__
+            req = base.__env_require__
             if isinstance(req, (list, tuple)):
                 r.update(req)
             else:

gof/opt.py

@@ -9,8 +9,6 @@ import ext
 
 class Optimizer:
 
-    __require__ = ()
-    
     def apply(self, env):
         pass
 
@@ -18,27 +16,6 @@ class Optimizer:
         env.satisfy(self)
         self.apply(env)
 
-    @classmethod
-    def require(cls):
-        """
-        Returns a list of EnvFeature subclasses that must be used by
-        any Env manipulating this kind of op. For instance, a
-        Destroyer requires features.DestroyHandler to guarantee that
-        various destructive operations don't interfere.
-        """
-        r = set()
-
-        bases = utils.all_bases(cls, lambda cls: hasattr(cls, '__require__'))
-        bases.append(cls)
-
-        for base in bases:
-            req = base.__require__
-            if isinstance(req, (list, tuple)):
-                r.update(req)
-            else:
-                r.add(req)
-        return r
-
     def __call__(self, env):
         self.optimize(env)
 
@@ -88,7 +65,7 @@ class LocalOptimizer(Optimizer):
 
 class OpSpecificOptimizer(LocalOptimizer):
 
-    __require__ = features.InstanceFinder
+    __env_require__ = features.InstanceFinder
 
     opclass = Op
 
@@ -100,7 +77,7 @@ class OpSpecificOptimizer(LocalOptimizer):
 
 class OpSubOptimizer(Optimizer):
 
-    __require__ = features.InstanceFinder
+    __env_require__ = features.InstanceFinder
 
     def __init__(self, op1, op2):
         if not op1.has_default_output:
@@ -127,7 +104,7 @@ class OpSubOptimizer(Optimizer):
 
 class OpRemover(Optimizer):
 
-    __require__ = features.InstanceFinder
+    __env_require__ = features.InstanceFinder
 
     def __init__(self, opclass):
         self.opclass = opclass