Python moves.reduce函数代码示例

    def __init__(self,proj_id):
        self.dtype = np.float32
        try:
            self.vg = projector.volume_geometry(proj_id)
            self.pg = projector.projection_geometry(proj_id)
            self.data_mod = data2d
            self.appendString = ""
            if projector.is_cuda(proj_id):
                self.appendString += "_CUDA"
        except Exception:
            self.vg = projector3d.volume_geometry(proj_id)
            self.pg = projector3d.projection_geometry(proj_id)
            self.data_mod = data3d
            self.appendString = "3D"
            if projector3d.is_cuda(proj_id):
                self.appendString += "_CUDA"

        self.vshape = functions.geom_size(self.vg)
        self.vsize = reduce(operator.mul,self.vshape)
        self.sshape = functions.geom_size(self.pg)
        self.ssize = reduce(operator.mul,self.sshape)

        self.shape = (self.ssize, self.vsize)

        self.proj_id = proj_id

        self.T = OpTomoTranspose(self)

def _get_merged_histograms(cursor, property_name, path, with_processes,
                           histograms_url, additional_histograms):
    if path[0] == "histograms" and len(path) != 2:
        raise ValueError("Histogram access requires a histogram name.")
    elif path[0] == "keyedHistograms" and len(path) != 3:
        raise ValueError("Keyed histogram access requires both a histogram name and a label.")

    # Get parent property
    parent = _get_ping_property(cursor, path, histograms_url,
                                additional_histograms)

    # Get children properties
    if not isinstance(cursor, dict):
        children = []
    else:
        children = [_get_ping_property(cursor["processes"]["content"],
                                       path, histograms_url,
                                       additional_histograms)]
        children += [_get_ping_property(child, path, histograms_url, additional_histograms)
                     for child in cursor.get("childPayloads", [])]
        children += [_get_ping_property(cursor["processes"]["gpu"],
                                        path, histograms_url,
                                        additional_histograms)]
        children = list(filter(lambda h: h is not None, children))

    # Merge parent and children
    merged = ([parent] if parent else []) + children

    result = {}
    if with_processes:
        result[property_name + "_parent"] = parent
        result[property_name + "_children"] = reduce(add, children) if children else None
    result[property_name] = reduce(add, merged) if merged else None

    return result

 def generate(operator, *args):
     """Generates SparseInterval instance current and past
     results of RecurrentEvent.forward() generators with respect
     to 'operator' of given expression."""
     operands = []
     for arg in args:
         if isinstance(arg, dict):
             try:
                 result = next(arg['generator'])
                 arg['results'] = (
                     arg['results'].union(SparseInterval(*[result]))
                 )
             except StopIteration:
                 arg['exhausted'] = True
             operands.append(arg['results'])
         elif isinstance(arg, SparseInterval):
             operands.append(arg)
     if operator == AND:
         return reduce(lambda m, v: m.intersection(v), operands)
     elif operator == OR:
         return reduce(lambda m, v: m.union(v), operands)
     elif operator == NOT:
         union = reduce(lambda m, v: m.union(v), operands)
         intervals = it.chain((MIN,),
                              it.chain.from_iterable(union.intervals),
                              (MAX,))
         return SparseInterval(*zip(intervals, intervals))
     else:
         raise AssertionError

    def reshape(self, *shape):
        """Gives a new shape to an array without changing its data."""

        # TODO: add more error-checking, perhaps
        if not self.flags.forc:
            raise RuntimeError("only contiguous arrays may "
                    "be used as arguments to this operation")

        if isinstance(shape[0], tuple) or isinstance(shape[0], list):
            shape = tuple(shape[0])

        if shape == self.shape:
            return self

        if -1 in shape:
            shape = list(shape)
            idx = shape.index(-1)
            size = -reduce(lambda x, y: x * y, shape, 1)
            shape[idx] = self.size // size
            if -1 in shape[idx:]:
                raise ValueError("can only specify one unknown dimension")
            shape = tuple(shape)

        size = reduce(lambda x, y: x * y, shape, 1)
        if size != self.size:
            raise ValueError("total size of new array must be unchanged")

        return GPUArray(
                shape=shape,
                dtype=self.dtype,
                allocator=self.allocator,
                base=self,
                gpudata=int(self.gpudata))

 def backtrack(self, node, tasks):
     candidates = self.fetch_tracks(node.op)
     tracks = []
     def filter(node, depth):
         new_candidates = []
         for candidate in candidates:
             track, i, lopt = candidate
             if i < depth:
                 pass
             elif track[i-depth] in (None, node.op):
                 if i == depth:
                     tasks[node].append(lopt)
                 else:
                     tracks.append(candidate)
             else:
                 new_candidates.append(candidate)
         return new_candidates
     depth = 0
     nodes = [node]
     while candidates:
         for node in nodes:
             candidates = list(filter(node, depth))
         depth += 1
         _nodes = nodes
         nodes = reduce(list.__iadd__,
                        [reduce(list.__iadd__,
                                [[n for n, i in out.clients if not isinstance(n, string_types)] for out in node.outputs],
                                []) for node in nodes],
                        [])
         candidates = tracks
         tracks = []

 def get_context_data(self, **kwargs):
     context = super(SearchableListMixin, self).get_context_data(**kwargs)
     qs = self.model.objects.all()
     total_objects = qs.count()
     query = self.get_search_query()
     is_filter = False
     if query:
         is_filter = True
         w_qs = []
         search_pairs = self.get_search_fields_with_filters()
         for word in self.get_words(query):
             filters = [Q(**{'%s__%s' % (pair[0], pair[1]): word}) for pair in search_pairs]
             if self.search_date_fields:
                 dt = self.try_convert_to_date(word)
                 if dt:
                     filters.extend([Q(**{field_name: dt}) for field_name in self.search_date_fields])
             w_qs.append(reduce(operator.or_, filters))
         qs = qs.filter(reduce(operator.and_, w_qs))
         qs = qs.distinct()
         total_objects = qs.count()
     paginator = Paginator(qs, 10)
     page = self.request.GET.get('page')
     try:
         qs = paginator.page(page)
     except PageNotAnInteger:
         qs = paginator.page(1)
     except EmptyPage:
         qs = paginator.page(paginator.num_pages)
     context.update({'object_list': qs, 'is_filter': is_filter, 'total_objects': total_objects})
     return context

 def __purity(cls, mem, references, macro=True, verbose=False):
     clusters = {}
     avg = 0
     memberships = [x for x in mem]
     for (i, x) in memberships:
         if x in clusters:
             clusters[x].append(i)
         else:
             clusters[x] = [i]
     for docs in clusters.values():
         (_, count) = median(reduce(lambda s, x: s + x, [references[x] for x in docs]))
         if verbose:
             print('# %f = %d/%d, labels %s for cluster %s' % (
                 float(count) / float(len(docs)),
                 count, len(docs),
                 str(reduce(lambda s, x: s + x, [references[x] for x in docs])),
                 str(docs)))
         if macro:
             avg += count
         else:
             avg += float(count) / float(len(docs))
     if macro:
         return float(avg) / len(memberships)
     else:
         return float(avg) / len(clusters)

    def as_list(self):
        # on top-level, I have type and depth, for easier parallelism,
        # but this is hacky as hell
        schema = (
            self.schema.items if isinstance(self.schema, ArraySchema)
            else self.schema
        )
        items = [InputAdapter(item, self.evaluator, schema)
                 for item in self.value]

        if not self.prefix:
            return reduce(operator.add, [a.arg_list() for a in items], [])

        if self.separate and self.item_separator is None:
            return reduce(operator.add, [[self.prefix] + a.arg_list()
                                         for a in items], [])

        if not self.separate and self.item_separator is None:
            return [self.prefix + a.list_item()
                    for a in items if a.list_item() is not None]

        joined = self.item_separator.join(
            filter(None, [a.list_item() for a in items])
        )

        if self.separate and self.item_separator is not None:
            return [self.prefix, joined]
        return [self.prefix + joined]

  def evaluate(self, previousResult, original=None, fast=False):
    original = original if original is not None else previousResult

    if original._grouped_self and fast:
      name = GetName(self)

      # TODO: Rewrite this, this is terrible.
      go_to_index = len(self._queue)
      for idx, item in enumerate(self._queue):
        if isinstance(item, OperatorStep):
          go_to_index = idx
          break

      transform_input = lambda x: reduce(
          lambda prevResult, f: f.evaluate(prevResult, original, fast=fast),
          self._queue[1:go_to_index],
          x
      )
      out = original._grouped_self[name].transform(transform_input)
      out = reduce(lambda prevResult, f: f.evaluate(prevResult, original, fast=fast),
                      self._queue[go_to_index:],
                      out)
      return out
    else:
      output = reduce(lambda prevResult, f: f.evaluate(prevResult, original),
                      self._queue,
                      original)
      return output

    def __init__(self,proj_id):
        self.dtype = np.float32
        try:
            self.vg = projector.volume_geometry(proj_id)
            self.pg = projector.projection_geometry(proj_id)
            self.data_mod = data2d
            self.appendString = ""
            if projector.is_cuda(proj_id):
                self.appendString += "_CUDA"
        except Exception:
            self.vg = projector3d.volume_geometry(proj_id)
            self.pg = projector3d.projection_geometry(proj_id)
            self.data_mod = data3d
            self.appendString = "3D"
            if projector3d.is_cuda(proj_id):
                self.appendString += "_CUDA"

        self.vshape = functions.geom_size(self.vg)
        self.vsize = reduce(operator.mul,self.vshape)
        self.sshape = functions.geom_size(self.pg)
        self.ssize = reduce(operator.mul,self.sshape)

        self.shape = (self.ssize, self.vsize)

        self.proj_id = proj_id

        self.transposeOpTomo = OpTomoTranspose(self)
        try:
            self.T = self.transposeOpTomo
        except AttributeError:
            # Scipy >= 0.16 defines self.T using self._transpose()
            pass

 def combineQualities(allowed_qualities, preferred_qualities):
     any_quality = 0
     best_quality = 0
     if allowed_qualities:
         any_quality = reduce(operator.or_, allowed_qualities)
     if preferred_qualities:
         best_quality = reduce(operator.or_, preferred_qualities)
     return any_quality | (best_quality << 16)

    def get_usage(self, using="short"):
        if using not in set(["short", "long", "both"]):
            raise ValueError(
                "using has to be 'short', 'long' or 'both'; not %r" % using
            )
        # The usage is generated in three steps:
        # 1. A "linked list" is generated with lists as nodes. Each node is a
        #    list consisting of an optional positional as first item followed by
        #    zero or more required positionals, optionally followed by a list
        #    representing the next node.
        #
        # 2. The usage string is generated by enclosing the with a space joined
        #    usage strings of the positionals in a given node with brackets.
        #    The exception is the root which is not enclosed by brackets as the
        #    first node is always required.
        #
        # 3. The usage string generated in `2` is prefixed with the short and/
        #    or the long and either or both joined with a comma are returned.

        def step(acc, next):
            root, current = acc
            if next.optional:
                current.append([next])
                current = current[-1]
            else:
                current.append(next)
            return root, current

        def render(tree, _root=True):
            if isinstance(tree, Positional):
                return tree.usage
            else:
                nodes = u(" ").join(render(node, _root=False) for node in tree)
                if _root:
                    return nodes
                return u("[{0}]").format(nodes)

        usage = render(reduce(step, self.positionals, ([], ) * 2)[0])
        if using == "both" and self.short and self.long:
            return (
                u("{0} {1}").format(self.short, usage).strip() +
                u(", ") +
                u("{0} {1}").format(self.long, usage).strip()
            )
        else:
            return u("{0} {{0}}").format(
                self.short if using == "short" and self.short or
                              using in set(["long", "both"]) and not self.long
                else self.long
            ).format(usage)

        return usage.format(
            short=self.short,
            long=self.long,
            usage=render(reduce(step, self.positionals, ([], ) * 2)[0])
        ).strip()

def lcm( *a ):
  """Least common multiple.

  Usage: lcm( [ 3, 4, 5 ] )
  or:    lcm( 3, 4, 5 )
  """

  if len( a ) > 1: return reduce( lcm2, a )
  if hasattr( a[0], "__iter__" ): return reduce( lcm2, a[0] )
  return a[0]

def gcd( *a ):
  """Greatest common divisor.

  Usage: gcd( [ 2, 4, 6 ] )
  or:    gcd( 2, 4, 6 )
  """

  if len( a ) > 1: return reduce( gcd2, a )
  if hasattr( a[0], "__iter__" ): return reduce( gcd2, a[0] )
  return a[0]

 def __setattr__(self, name, value):
     if isinstance(self._parent, Style.Style):
         setattr(
             reduce(getattr, self._attribute.split('.'), self._parent),
             name, value)
     else:
         for cell in self._parent:
             setattr(
                 reduce(getattr, self._attribute.split('.'), cell),
                 name, value)