but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License and the GNU Lesser General Public License
for more details.
You should have received a copy of the GNU General Public License
and the GNU Lesser General Public License along with pyDive.
If not, see <http://www.gnu.org/licenses/>.
"""
__doc__ = None
import numpy as np
import pyDive.distribution.multiple_axes as multiple_axes
from pyDive.distribution.interengine import MPI_copier
ndarray = multiple_axes.distribute(np.ndarray, "ndarray", "np", interengine_copier=MPI_copier)
factories = multiple_axes.generate_factories(ndarray, ("empty", "zeros", "ones"), np.float)
factories.update(multiple_axes.generate_factories_like(ndarray, ("empty_like", "zeros_like", "ones_like")))
globals().update(factories)
def array(array_like, distaxes='all'):
"""Create a pyDive.ndarray instance from an array-like object.
:param array_like: Any object exposing the array interface, e.g. numpy-array, python sequence, ...
:param ints distaxes: distributed axes. Defaults to 'all' meaning each axis is distributed.
"""
np_array = np.array(array_like)
result = empty(np_array.shape, np_array.dtype, distaxes)
result[:] = np_array
You should have received a copy of the GNU General Public License
and the GNU Lesser General Public License along with pyDive.
If not, see <http://www.gnu.org/licenses/>.
"""
__doc__ = None
import numpy as np
import adios as ad
from pyDive.arrays.ndarray import hollow_like
import pyDive.distribution.multiple_axes as multiple_axes
import pyDive.IPParallelClient as com
from itertools import islice
from .. import structured
import pyDive.arrays.local.ad_ndarray
ad_ndarray = multiple_axes.distribute(pyDive.arrays.local.ad_ndarray.ad_ndarray, "ad_ndarray", "ad_ndarray", may_allocate=False)
def load(self):
"""Load array from file into main memory of all engines in parallel.
:return: pyDive.ndarray instance
"""
result = hollow_like(self)
view = com.getView()
view.execute("{0} = {1}.load()".format(result.name, self.name), targets=result.target_ranks)
return result
ad_ndarray.load = load
del load
def open_variable(filename, variable_path, distaxis=0):
"""Create a pyDive.adios.ad_ndarray instance from file.
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License and the GNU Lesser General Public License
for more details.
You should have received a copy of the GNU General Public License
and the GNU Lesser General Public License along with pyDive.
If not, see <http://www.gnu.org/licenses/>.
"""
__doc__ = None
import numpy as np
import pyDive.distribution.multiple_axes as multiple_axes
from pyDive.distribution.interengine import GPU_copier
import pyDive.arrays.local.gpu_ndarray
gpu_ndarray = multiple_axes.distribute(pyDive.arrays.local.gpu_ndarray.gpu_ndarray, "gpu_ndarray",\
"pyDive.arrays.local.gpu_ndarray", interengine_copier=GPU_copier)
factories = multiple_axes.generate_factories(gpu_ndarray, ("empty", "zeros"),
np.float)
factories.update(
multiple_axes.generate_factories_like(gpu_ndarray,
("empty_like", "zeros_like")))
globals().update(factories)
def ones(shape, dtype=np.float, distaxes='all', **kwargs):
result = zeros(shape, dtype, distaxes, **kwargs)
result += 1
return result
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License and the GNU Lesser General Public License
for more details.
You should have received a copy of the GNU General Public License
and the GNU Lesser General Public License along with pyDive.
If not, see <http://www.gnu.org/licenses/>.
"""
__doc__ = None
import numpy as np
import pyDive.distribution.multiple_axes as multiple_axes
from pyDive.distribution.interengine import MPI_copier
ndarray = multiple_axes.distribute(np.ndarray,
"ndarray",
"np",
interengine_copier=MPI_copier)
factories = multiple_axes.generate_factories(ndarray,
("empty", "zeros", "ones"),
np.float)
factories.update(
multiple_axes.generate_factories_like(
ndarray, ("empty_like", "zeros_like", "ones_like")))
globals().update(factories)
def array(array_like, distaxes='all'):
"""Create a pyDive.ndarray instance from an array-like object.
for more details.
You should have received a copy of the GNU General Public License
and the GNU Lesser General Public License along with pyDive.
If not, see <http://www.gnu.org/licenses/>.
"""
__doc__ = None
import numpy as np
import pyDive.distribution.multiple_axes as multiple_axes
from pyDive.distribution.interengine import GPU_copier
import pyDive.arrays.local.gpu_ndarray
gpu_ndarray = multiple_axes.distribute(
pyDive.arrays.local.gpu_ndarray.gpu_ndarray,
"gpu_ndarray",
"pyDive.arrays.local.gpu_ndarray",
interengine_copier=GPU_copier,
)
factories = multiple_axes.generate_factories(gpu_ndarray, ("empty", "zeros"), np.float)
factories.update(multiple_axes.generate_factories_like(gpu_ndarray, ("empty_like", "zeros_like")))
globals().update(factories)
def ones(shape, dtype=np.float, distaxes="all", **kwargs):
result = zeros(shape, dtype, distaxes, **kwargs)
result += 1
return result
def ones_like(other, **kwargs):