def new_block(self, op_name, grid_entry, grid_meta):
op_func = np.__getattribute__(op_name)
grid = ArrayGrid.from_meta(grid_meta)
block_shape = grid.get_block_shape(grid_entry)
if op_name == "eye":
assert np.all(np.diff(grid_entry) == 0)
return op_func(*block_shape, dtype=grid.dtype)
else:
return op_func(block_shape, dtype=grid.dtype)
def csc(self):
meta_swap = self.grid.to_meta()
grid_swap = ArrayGrid.from_meta(meta_swap)
rarr_src = np.ndarray(self.blocks.shape, dtype='O')
for grid_entry in self.grid.get_entry_iterator():
rarr_src[grid_entry] = self.blocks[grid_entry].csc()
rarr_swap = SparseBlockArray(grid_swap, self.system, rarr_src)
return rarr_swap
def read_fs(self, filename: str):
meta = self._filesystem.read_meta_fs(filename)
addresses = meta["addresses"]
grid_meta = meta["grid_meta"]
grid = ArrayGrid.from_meta(grid_meta)
ba: BlockArray = BlockArray(grid, self._system)
for grid_entry in addresses:
node_address = addresses[grid_entry]
options = {"resources": {node_address: 1.0 / 10**4}}
ba.blocks[grid_entry].oid = self._filesystem.read_block_fs(
filename, grid_entry, grid_meta, options=options)
return ba
def __getattr__(self, item):
if item != "T":
raise NotImplementedError(item)
metaT = self.grid.to_meta()
metaT["shape"] = tuple(reversed(metaT["shape"]))
metaT["block_shape"] = tuple(reversed(metaT["block_shape"]))
result_grid: ArrayGrid = ArrayGrid.from_meta(metaT)
result_graphs = np.copy(self.graphs.T)
for grid_entry in result_grid.get_entry_iterator():
self._add_uop("transpose", grid_entry, result_graphs,
result_graphs)
return GraphArray(result_grid,
self.cluster_state,
result_graphs,
copy_on_op=self.copy_on_op)
def __getattr__(self, item):
if item == "__array_priority__" or item == "__array_struct__":
# This is triggered by a numpy array on the LHS.
raise ValueError("Unable to covert numpy array to block array.")
if item != "T":
raise NotImplementedError(item)
metaT = self.grid.to_meta()
metaT["shape"] = tuple(reversed(metaT["shape"]))
metaT["block_shape"] = tuple(reversed(metaT["block_shape"]))
gridT = ArrayGrid.from_meta(metaT)
rarrT = BlockArray(gridT, self.system)
rarrT.blocks = np.copy(self.blocks.T)
for grid_entry in rarrT.grid.get_entry_iterator():
rarrT.blocks[grid_entry] = rarrT.blocks[grid_entry].transpose()
return rarrT
def delete_fs(self, filename: str):
meta = self._filesystem.read_meta_fs(filename)
addresses = meta["addresses"]
grid_meta = meta["grid_meta"]
grid = ArrayGrid.from_meta(grid_meta)
result_grid = ArrayGrid(grid.grid_shape,
tuple(np.ones_like(grid.shape, dtype=np.int)),
dtype=dict.__name__)
rarr = BlockArray(result_grid, self._system)
for grid_entry in addresses:
node_address = addresses[grid_entry]
options = {"resources": {node_address: 1.0 / 10**4}}
rarr.blocks[grid_entry].oid = self._filesystem.delete_block_fs(
filename, grid_entry, grid_meta, options=options)
self._filesystem.delete_meta_fs(filename)
return rarr
def swapaxes(self, axis1, axis2):
meta_swap = self.grid.to_meta()
shape = list(meta_swap["shape"])
block_shape = list(meta_swap["block_shape"])
dim = len(shape)
if axis1 >= dim or axis2 >= dim:
raise ValueError("axis is larger than the array dimension")
shape[axis1], shape[axis2] = shape[axis2], shape[axis1]
block_shape[axis1], block_shape[axis2] = block_shape[axis2], block_shape[axis1]
meta_swap["shape"] = tuple(shape)
meta_swap["block_shape"] = tuple(block_shape)
grid_swap = ArrayGrid.from_meta(meta_swap)
rarr_src = np.ndarray(self.blocks.shape, dtype='O')
for grid_entry in self.grid.get_entry_iterator():
rarr_src[grid_entry] = self.blocks[grid_entry].swapaxes(axis1, axis2)
rarr_src = rarr_src.swapaxes(axis1, axis2)
rarr_swap = BlockArray(grid_swap, self.system, rarr_src)
return rarr_swap
def empty(self, grid_entry, grid_meta):
grid = ArrayGrid.from_meta(grid_meta)
block_shape = grid.get_block_shape(grid_entry)
return np.empty(block_shape, dtype=grid.dtype)
def delete_block_s3(filename: AnyStr, grid_entry: Tuple, grid_meta: Dict):
return np.array(StoredArrayS3(
filename, ArrayGrid.from_meta(grid_meta)).delete(grid_entry),
dtype=dict)
def read_block_s3(filename: AnyStr, grid_entry: Tuple, grid_meta: Dict):
return StoredArrayS3(filename,
ArrayGrid.from_meta(grid_meta)).get(grid_entry)
def write_block_s3(block: Any, filename: AnyStr, grid_entry: Tuple,
grid_meta: Dict):
return np.array(StoredArrayS3(filename,
ArrayGrid.from_meta(grid_meta)).put(
grid_entry, block),
dtype=dict)
def write_meta_s3(filename: AnyStr, grid_meta: Dict):
sa: StoredArrayS3 = StoredArrayS3(filename, ArrayGrid.from_meta(grid_meta))
return np.array(sa.put_grid(sa.grid), dtype=dict)
