def test_from_global_dim_data_bc(self):
""" Test creation of a block-cyclic array. """
rows, cols = 5, 9
global_dim_data = (
# dim 0
{
'dist_type': 'c',
'proc_grid_size': 2,
'size': rows,
'block_size': 2,
},
# dim 1
{
'dist_type': 'c',
'proc_grid_size': 2,
'size': cols,
'block_size': 2,
},
)
distribution = Distribution.from_global_dim_data(
self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
las = distarr.get_localarrays()
local_shapes = [la.local_shape for la in las]
self.assertSequenceEqual(local_shapes, [(3, 5), (3, 4), (2, 5),
(2, 4)])
def test_from_global_dim_data_irregular_block(self):
bounds = (0, 2, 3, 4, 10)
glb_dim_data = ({"dist_type": "b", "bounds": bounds},)
distribution = Distribution.from_global_dim_data(self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test___init__(self):
shape = (5, 5)
distribution = Distribution(self.context, shape, ('b', 'c'))
da = DistArray(distribution, dtype=int)
da.fill(42)
nda = numpy.empty(shape, dtype=int)
nda.fill(42)
assert_array_equal(da.tondarray(), nda)
def test_from_global_dim_data_irregular_block(self):
bounds = (0, 2, 3, 4, 10)
glb_dim_data = ({'dist_type': 'b', 'bounds': bounds}, )
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test___init__(self):
shape = (5, 5)
distribution = Distribution(self.context, shape, ("b", "c"))
da = DistArray(distribution, dtype=int)
da.fill(42)
nda = numpy.empty(shape, dtype=int)
nda.fill(42)
assert_array_equal(da.tondarray(), nda)
def test_from_global_dim_data_uu(self):
rows = 6
cols = 20
row_ixs = numpy.random.permutation(range(rows))
col_ixs = numpy.random.permutation(range(cols))
row_indices = [row_ixs[: rows // 2], row_ixs[rows // 2 :]]
col_indices = [col_ixs[: cols // 4], col_ixs[cols // 4 :]]
glb_dim_data = ({"dist_type": "u", "indices": row_indices}, {"dist_type": "u", "indices": col_indices})
distribution = Distribution.from_global_dim_data(self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_irregular_block_assignment(self):
global_dim_data = ({
'dist_type': 'b',
'bounds': (0, 5),
}, {
'dist_type': 'b',
'bounds': (0, 2, 6, 7, 9),
})
distribution = Distribution.from_global_dim_data(
self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_from_global_dim_data_bu(self):
rows = 9
row_break_point = rows // 2
cols = 10
col_indices = numpy.random.permutation(range(cols))
col_break_point = len(col_indices) // 3
indices = [col_indices[:col_break_point], col_indices[col_break_point:]]
glb_dim_data = (
{"dist_type": "b", "bounds": (0, row_break_point, rows)},
{"dist_type": "u", "indices": indices},
)
distribution = Distribution.from_global_dim_data(self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def load_npy(self, filename, distribution):
"""
Load a DistArray from a dataset in a ``.npy`` file.
Parameters
----------
filename : str
Filename to load.
distribution: Distribution object
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded.
"""
def _local_load_npy(filename, ddpr, comm):
from distarray.localapi import load_npy
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_npy(comm, filename, dim_data))
ddpr = distribution.get_dim_data_per_rank()
da_key = self.apply(_local_load_npy,
(filename, ddpr, distribution.comm),
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def proxy_func(a, b, *args, **kwargs):
context = determine_context(a, b)
is_a_dap = isinstance(a, DistArray)
is_b_dap = isinstance(b, DistArray)
if is_a_dap and is_b_dap:
if not a.distribution.is_compatible(b.distribution):
raise ValueError("distributions not compatible.")
a_key = a.key
b_key = b.key
distribution = a.distribution
elif is_a_dap and numpy.isscalar(b):
a_key = a.key
b_key = b
distribution = a.distribution
elif is_b_dap and numpy.isscalar(a):
a_key = a
b_key = b.key
distribution = b.distribution
else:
raise TypeError("only DistArray or scalars are accepted")
def func_call(func_name, a, b, args, kwargs):
from distarray.utils import get_from_dotted_name
dotted_name = "distarray.localapi.%s" % (func_name,)
func = get_from_dotted_name(dotted_name)
res = func(a, b, *args, **kwargs)
return proxyize(res), res.dtype # noqa
res = context.apply(func_call, args=(name, a_key, b_key, args, kwargs), targets=distribution.targets)
new_key = res[0][0]
dtype = res[0][1]
return DistArray.from_localarrays(new_key, distribution=distribution, dtype=dtype)
def test_from_global_dim_data_1d(self):
total_size = 40
list_of_indices = [
[29, 38, 18, 19, 11, 33, 10, 1, 22, 25],
[5, 15, 34, 12, 16, 24, 23, 39, 6, 36],
[0, 7, 27, 4, 32, 37, 21, 26, 9, 17],
[35, 14, 20, 13, 3, 30, 2, 8, 28, 31],
]
glb_dim_data = ({"dist_type": "u", "indices": list_of_indices},)
distribution = Distribution.from_global_dim_data(self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
for i in range(total_size):
distarr[i] = i
localarrays = distarr.get_localarrays()
for i, arr in enumerate(localarrays):
assert_allclose(arr, list_of_indices[i])
def test_from_global_dim_data_bc(self):
""" Test creation of a block-cyclic array. """
rows, cols = 5, 9
global_dim_data = (
# dim 0
{"dist_type": "c", "proc_grid_size": 2, "size": rows, "block_size": 2},
# dim 1
{"dist_type": "c", "proc_grid_size": 2, "size": cols, "block_size": 2},
)
distribution = Distribution.from_global_dim_data(self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
las = distarr.get_localarrays()
local_shapes = [la.local_shape for la in las]
self.assertSequenceEqual(local_shapes, [(3, 5), (3, 4), (2, 5), (2, 4)])
def load_npy(self, filename, distribution):
"""
Load a DistArray from a dataset in a ``.npy`` file.
Parameters
----------
filename : str
Filename to load.
distribution: Distribution object
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded.
"""
def _local_load_npy(filename, ddpr, comm):
from distarray.localapi import load_npy
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_npy(comm, filename, dim_data))
ddpr = distribution.get_dim_data_per_rank()
da_key = self.apply(_local_load_npy, (filename, ddpr, distribution.comm),
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def distributed_julia_calc(la, la2, kernel=numpy_julia_calc):
context = la.context
context.register(kernel)
iters_key = context.apply(local_julia_calc, (la.key, la2.key), {'kernel': kernel})
iters_da = DistArray.from_localarrays(iters_key[0], context=context, dtype=np.int32)
#return iters_da
return iters_da
def distributed_julia_calc(distarray, c, z_max, n_max,
kernel=fancy_numpy_julia_calc):
"""Calculate the Julia set for an array of points in the complex plane.
Parameters
----------
distarray : DistArray
DistArray of complex values whose iterations we will count.
c : complex
Complex number to add at each iteration.
z_max : float
Magnitude of complex value that we assume goes to infinity.
n_max : int
Maximum number of iterations.
kernel: function
Kernel to use for computation of the Julia set. Options are 'fancy',
'numpy', or 'cython'.
"""
context = distarray.context
iters_key = context.apply(local_julia_calc,
(distarray.key, c, z_max, n_max),
{'kernel': kernel})
iters_da = DistArray.from_localarrays(iters_key[0], context=context,
dtype=numpy.int32)
return iters_da
def _process_local_results(self, results, targets):
"""Figure out what to return on the Client.
Parameters
----------
key : string
Key corresponding to wrapped function's return value.
Returns
-------
Varied
A DistArray (if locally all values are LocalArray), a None (if
locally all values are None), or else, pull the result back to the
client and return it. If all but one of the pulled values is None,
return that non-None value only.
"""
def is_NoneType(pxy):
return pxy.type_str == str(type(None))
def is_LocalArray(pxy):
return (isinstance(pxy, Proxy) and
pxy.type_str == "<class 'distarray.localapi.localarray.LocalArray'>")
if all(is_LocalArray(r) for r in results):
result = DistArray.from_localarrays(results[0], context=self, targets=targets)
elif all(r is None for r in results):
result = None
else:
if has_exactly_one(results):
result = next(x for x in results if x is not None)
else:
result = results
return result
def distributed_julia_calc(distarray,
c,
z_max,
n_max,
kernel=fancy_numpy_julia_calc):
"""Calculate the Julia set for an array of points in the complex plane.
Parameters
----------
distarray : DistArray
DistArray of complex values whose iterations we will count.
c : complex
Complex number to add at each iteration.
z_max : float
Magnitude of complex value that we assume goes to infinity.
n_max : int
Maximum number of iterations.
kernel: function
Kernel to use for computation of the Julia set. Options are 'fancy',
'numpy', or 'cython'.
"""
context = distarray.context
iters_key = context.apply(local_julia_calc,
(distarray.key, c, z_max, n_max),
{'kernel': kernel})
iters_da = DistArray.from_localarrays(iters_key[0],
context=context,
dtype=numpy.int32)
return iters_da
def distributed_process(data, output, processes, process, params, kernel=timeseries_correction_set_up):
print "IN THE DISTRIBUTED_PROCESS FUNCTION"
context = data.context
context.register(kernel)
iters_key = context.apply(local_process, (data.key, params), {'kernel': kernel})
output = da.from_localarrays(iters_key[0], context=context, dtype=np.int32)
return output
def distributed_julia_calc(distarray, c, z_max, n_max, kernel=numpy_julia_calc):
context = distarray.context
iters_key = context.apply(local_julia_calc,
(distarray.key, c, z_max, n_max),
{'kernel': kernel})
iters_da = DistArray.from_localarrays(iters_key[0], context=context,
dtype=numpy.int32)
return iters_da
def test_from_global_dim_data_1d(self):
total_size = 40
list_of_indices = [
[29, 38, 18, 19, 11, 33, 10, 1, 22, 25],
[5, 15, 34, 12, 16, 24, 23, 39, 6, 36],
[0, 7, 27, 4, 32, 37, 21, 26, 9, 17],
[35, 14, 20, 13, 3, 30, 2, 8, 28, 31],
]
glb_dim_data = ({
'dist_type': 'u',
'indices': list_of_indices,
}, )
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
for i in range(total_size):
distarr[i] = i
localarrays = distarr.get_localarrays()
for i, arr in enumerate(localarrays):
assert_allclose(arr, list_of_indices[i])
def test_from_global_dim_data_uu(self):
rows = 6
cols = 20
row_ixs = numpy.random.permutation(range(rows))
col_ixs = numpy.random.permutation(range(cols))
row_indices = [row_ixs[:rows // 2], row_ixs[rows // 2:]]
col_indices = [col_ixs[:cols // 4], col_ixs[cols // 4:]]
glb_dim_data = (
{
'dist_type': 'u',
'indices': row_indices
},
{
'dist_type': 'u',
'indices': col_indices
},
)
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_from_global_dim_data_bu(self):
rows = 9
row_break_point = rows // 2
cols = 10
col_indices = numpy.random.permutation(range(cols))
col_break_point = len(col_indices) // 3
indices = [
col_indices[:col_break_point], col_indices[col_break_point:]
]
glb_dim_data = (
{
'dist_type': 'b',
'bounds': (0, row_break_point, rows)
},
{
'dist_type': 'u',
'indices': indices
},
)
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def proxy_func(a, *args, **kwargs):
context = determine_context(a)
def func_call(func_name, arr_name, args, kwargs):
from distarray.utils import get_from_dotted_name
dotted_name = "distarray.localapi.%s" % (func_name,)
func = get_from_dotted_name(dotted_name)
res = func(arr_name, *args, **kwargs)
return proxyize(res), res.dtype # noqa
res = context.apply(func_call, args=(name, a.key, args, kwargs), targets=a.targets)
new_key = res[0][0]
dtype = res[0][1]
return DistArray.from_localarrays(new_key, distribution=a.distribution, dtype=dtype)
def process(self, plugin, in_data, out_data, processes, process, params, kernel):
if kernel is "timeseries_correction_set_up":
kernel = du.timeseries_correction_set_up
elif kernel is "reconstruction_set_up":
kernel = du.reconstruction_set_up
elif kernel is "filter_set_up":
kernel = du.filter_set_up
else:
print("The kernel", kernel, "has not been registered in dist_array_transport")
sys.exit(1)
iters_key = du.distributed_process(plugin, in_data, out_data, processes,
process, params, kernel)
out_data.data = da.from_localarrays(iters_key[0],
context=in_data.data.context,
dtype=np.int32)
def proxy_func(a, *args, **kwargs):
context = determine_context(a)
def func_call(func_name, arr_name, args, kwargs):
from distarray.utils import get_from_dotted_name
dotted_name = 'distarray.localapi.%s' % (func_name, )
func = get_from_dotted_name(dotted_name)
res = func(arr_name, *args, **kwargs)
return proxyize(res), res.dtype # noqa
res = context.apply(func_call,
args=(name, a.key, args, kwargs),
targets=a.targets)
new_key = res[0][0]
dtype = res[0][1]
return DistArray.from_localarrays(new_key,
distribution=a.distribution,
dtype=dtype)
def fromfunction(self, function, shape, **kwargs):
"""Create a DistArray from a function over global indices.
Unlike numpy's `fromfunction`, the result of distarray's
`fromfunction` is restricted to the same Distribution as the
index array generated from `shape`.
See numpy.fromfunction for more details.
"""
self.push_function(function.__name__, function, targets=self.targets)
def _local_fromfunction(func_name, comm, ddpr, kwargs):
from distarray.localapi import fromfunction
from distarray.localapi.maps import Distribution
from importlib import import_module
main = import_module('__main__')
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
func = getattr(main, func_name)
dist = Distribution(comm, dim_data=dim_data)
local_arr = fromfunction(func, dist, **kwargs)
return proxyize(local_arr)
dist = kwargs.get('dist', None)
grid_shape = kwargs.get('grid_shape', None)
distribution = Distribution(context=self,
shape=shape,
dist=dist,
grid_shape=grid_shape)
ddpr = distribution.get_dim_data_per_rank()
da_name = self.apply(
_local_fromfunction,
(function.__name__, distribution.comm, ddpr, kwargs),
targets=distribution.targets)
return DistArray.from_localarrays(da_name[0],
distribution=distribution)
def fromfunction(self, function, shape, **kwargs):
"""Create a DistArray from a function over global indices.
Unlike numpy's `fromfunction`, the result of distarray's
`fromfunction` is restricted to the same Distribution as the
index array generated from `shape`.
See numpy.fromfunction for more details.
"""
self.push_function(function.__name__, function, targets=self.targets)
def _local_fromfunction(func_name, comm, ddpr, kwargs):
from distarray.localapi import fromfunction
from distarray.localapi.maps import Distribution
from importlib import import_module
main = import_module('__main__')
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
func = getattr(main, func_name)
dist = Distribution(comm, dim_data=dim_data)
local_arr = fromfunction(func, dist, **kwargs)
return proxyize(local_arr)
dist = kwargs.get('dist', None)
grid_shape = kwargs.get('grid_shape', None)
distribution = Distribution(context=self,
shape=shape, dist=dist,
grid_shape=grid_shape)
ddpr = distribution.get_dim_data_per_rank()
da_name = self.apply(_local_fromfunction,
(function.__name__, distribution.comm, ddpr, kwargs),
targets=distribution.targets)
return DistArray.from_localarrays(da_name[0],
distribution=distribution)
def _create_local(self, local_call, shape_or_dist, dtype):
"""Creates LocalArrays with the method named in `local_call`."""
def create_local(local_call, ddpr, dtype, comm):
from distarray.localapi.maps import Distribution
if len(ddpr) == 0:
dim_data = ()
else:
dim_data = ddpr[comm.Get_rank()]
local_call = eval(local_call)
distribution = Distribution(comm=comm, dim_data=dim_data)
rval = local_call(distribution=distribution, dtype=dtype)
return proxyize(rval)
distribution = asdistribution(self, shape_or_dist)
ddpr = distribution.get_dim_data_per_rank()
args = [local_call, ddpr, dtype, distribution.comm]
da_key = self.apply(create_local, args=args,
targets=distribution.targets)[0]
return DistArray.from_localarrays(da_key, distribution=distribution,
dtype=dtype)
def _local_rand_call(self, local_func_name, shape_or_dist, kwargs=None):
kwargs = kwargs or {}
def _local_call(comm, local_func_name, ddpr, kwargs):
import distarray.localapi.random as local_random
from distarray.localapi.maps import Distribution
local_func = getattr(local_random, local_func_name)
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
dist = Distribution(dim_data=dim_data, comm=comm)
return proxyize(local_func(distribution=dist, **kwargs))
distribution = asdistribution(self.context, shape_or_dist)
ddpr = distribution.get_dim_data_per_rank()
args = (distribution.comm, local_func_name, ddpr, kwargs)
da_key = self.context.apply(_local_call, args,
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def load_hdf5(self, filename, distribution, key='buffer'):
"""
Load a DistArray from a dataset in an ``.hdf5`` file.
Parameters
----------
filename : str
Filename to load.
distribution: Distribution object
key : str, optional
The identifier for the group to load the DistArray from (the
default is 'buffer').
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded.
"""
try:
import h5py
except ImportError:
errmsg = "An MPI-enabled h5py must be available to use load_hdf5."
raise ImportError(errmsg)
def _local_load_hdf5(filename, ddpr, comm, key):
from distarray.localapi import load_hdf5
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_hdf5(comm, filename, dim_data, key))
ddpr = distribution.get_dim_data_per_rank()
da_key = self.apply(_local_load_hdf5,
(filename, ddpr, distribution.comm, key),
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def load_hdf5(self, filename, distribution, key='buffer'):
"""
Load a DistArray from a dataset in an ``.hdf5`` file.
Parameters
----------
filename : str
Filename to load.
distribution: Distribution object
key : str, optional
The identifier for the group to load the DistArray from (the
default is 'buffer').
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded.
"""
try:
import h5py
except ImportError:
errmsg = "An MPI-enabled h5py must be available to use load_hdf5."
raise ImportError(errmsg)
def _local_load_hdf5(filename, ddpr, comm, key):
from distarray.localapi import load_hdf5
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_hdf5(comm, filename, dim_data, key))
ddpr = distribution.get_dim_data_per_rank()
da_key = self.apply(_local_load_hdf5, (filename, ddpr, distribution.comm, key),
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def _create_local(self, local_call, shape_or_dist, dtype):
"""Creates LocalArrays with the method named in `local_call`."""
def create_local(local_call, ddpr, dtype, comm):
from distarray.localapi.maps import Distribution
if len(ddpr) == 0:
dim_data = ()
else:
dim_data = ddpr[comm.Get_rank()]
local_call = eval(local_call)
distribution = Distribution(comm=comm, dim_data=dim_data)
rval = local_call(distribution=distribution, dtype=dtype)
return proxyize(rval)
distribution = asdistribution(self, shape_or_dist)
ddpr = distribution.get_dim_data_per_rank()
args = [local_call, ddpr, dtype, distribution.comm]
da_key = self.apply(create_local,
args=args,
targets=distribution.targets)[0]
return DistArray.from_localarrays(da_key,
distribution=distribution,
dtype=dtype)
def _local_rand_call(self, local_func_name, shape_or_dist, kwargs=None):
kwargs = kwargs or {}
def _local_call(comm, local_func_name, ddpr, kwargs):
import distarray.localapi.random as local_random
from distarray.localapi.maps import Distribution
local_func = getattr(local_random, local_func_name)
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
dist = Distribution(dim_data=dim_data, comm=comm)
return proxyize(local_func(distribution=dist, **kwargs))
distribution = asdistribution(self.context, shape_or_dist)
ddpr = distribution.get_dim_data_per_rank()
args = (distribution.comm, local_func_name, ddpr, kwargs)
da_key = self.context.apply(_local_call,
args,
targets=distribution.targets)
return DistArray.from_localarrays(da_key[0], distribution=distribution)
def _process_local_results(self, results, targets):
"""Figure out what to return on the Client.
Parameters
----------
key : string
Key corresponding to wrapped function's return value.
Returns
-------
Varied
A DistArray (if locally all values are LocalArray), a None (if
locally all values are None), or else, pull the result back to the
client and return it. If all but one of the pulled values is None,
return that non-None value only.
"""
def is_NoneType(pxy):
return pxy.type_str == str(type(None))
def is_LocalArray(pxy):
return (isinstance(pxy, Proxy) and pxy.type_str
== "<class 'distarray.localapi.localarray.LocalArray'>")
if all(is_LocalArray(r) for r in results):
result = DistArray.from_localarrays(results[0],
context=self,
targets=targets)
elif all(r is None for r in results):
result = None
else:
if has_exactly_one(results):
result = next(x for x in results if x is not None)
else:
result = results
return result
def proxy_func(a, b, *args, **kwargs):
context = determine_context(a, b)
is_a_dap = isinstance(a, DistArray)
is_b_dap = isinstance(b, DistArray)
if is_a_dap and is_b_dap:
if not a.distribution.is_compatible(b.distribution):
raise ValueError("distributions not compatible.")
a_key = a.key
b_key = b.key
distribution = a.distribution
elif is_a_dap and numpy.isscalar(b):
a_key = a.key
b_key = b
distribution = a.distribution
elif is_b_dap and numpy.isscalar(a):
a_key = a
b_key = b.key
distribution = b.distribution
else:
raise TypeError('only DistArray or scalars are accepted')
def func_call(func_name, a, b, args, kwargs):
from distarray.utils import get_from_dotted_name
dotted_name = 'distarray.localapi.%s' % (func_name, )
func = get_from_dotted_name(dotted_name)
res = func(a, b, *args, **kwargs)
return proxyize(res), res.dtype # noqa
res = context.apply(func_call,
args=(name, a_key, b_key, args, kwargs),
targets=distribution.targets)
new_key = res[0][0]
dtype = res[0][1]
return DistArray.from_localarrays(new_key,
distribution=distribution,
dtype=dtype)
def with_context_and_dtype(self):
da = DistArray.from_localarrays(self.distarray.key,
context=self.context,
dtype=int)
assert_array_equal(da.toarray(), self.expected)
def with_distribution_and_dtype(self):
da = DistArray.from_localarrays(self.distarray.key,
distribution=self.distribution,
dtype=int)
assert_array_equal(da.toarray(), self.expected)
def with_distribution_and_context(self):
with self.assertRaise(RuntimeError):
DistArray.from_localarrays(self.distarray.key,
context=self.context,
distribution=self.distribution)
def with_distribution_and_context(self):
with self.assertRaise(RuntimeError):
DistArray.from_localarrays(self.distarray.key, context=self.context, distribution=self.distribution)
def load_dnpy(self, name):
"""
Load a distributed array from ``.dnpy`` files.
The ``.dnpy`` file format is a binary format inspired by NumPy's
``.npy`` format. The header of a particular ``.dnpy`` file contains
information about which portion of a DistArray is saved in it (using
the metadata outlined in the Distributed Array Protocol), and the data
portion contains the output of NumPy's `save` function for the local
array data. See the module docstring for `distarray.localapi.format` for
full details.
Parameters
----------
name : str or list of str
If a str, this is used as the prefix for the filename used by each
engine. Each engine will load a file named ``<name>_<rank>.dnpy``.
If a list of str, each engine will use the name at the index
corresponding to its rank. An exception is raised if the length of
this list is not the same as the context's communicator's size.
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded on each engine.
Raises
------
TypeError
If `name` is an iterable whose length is different from the
context's communicator's size.
See Also
--------
save_dnpy : Saving files to load with with load_dnpy.
"""
def _local_load_dnpy(comm, fname_base):
from distarray.localapi import load_dnpy
fname = "%s_%s.dnpy" % (fname_base, comm.Get_rank())
local_arr = load_dnpy(comm, fname)
return proxyize(local_arr)
def _local_load_dnpy_names(comm, fnames):
from distarray.localapi import load_dnpy
fname = fnames[comm.Get_rank()]
local_arr = load_dnpy(comm, fname)
return proxyize(local_arr)
if isinstance(name, six.string_types):
func = _local_load_dnpy
elif isinstance(name, collections.Sequence):
if len(name) != len(self.targets):
errmsg = "`name` must be the same length as `self.targets`."
raise TypeError(errmsg)
func = _local_load_dnpy_names
else:
errmsg = "`name` must be a string or a list."
raise TypeError(errmsg)
da_key = self.apply(func, (self.comm, name), targets=self.targets)
return DistArray.from_localarrays(da_key[0], context=self)
def distributed_filter(distarray, local_filter):
''' Filter a DistArray, returning a new DistArray. '''
context = distarray.context
filtered_key = context.apply(local_filter, (distarray.key, ))
filtered_da = DistArray.from_localarrays(filtered_key[0], context=context)
return filtered_da
def with_context_and_dtype(self):
da = DistArray.from_localarrays(self.distarray.key, context=self.context, dtype=int)
assert_array_equal(da.toarray(), self.expected)
def with_distribution_and_dtype(self):
da = DistArray.from_localarrays(self.distarray.key, distribution=self.distribution, dtype=int)
assert_array_equal(da.toarray(), self.expected)
def distributed_filter(distarray, local_filter):
''' Filter a DistArray, returning a new DistArray. '''
context = distarray.context
filtered_key = context.apply(local_filter, (distarray.key,))
filtered_da = DistArray.from_localarrays(filtered_key[0], context=context)
return filtered_da
def test_irregular_block_assignment(self):
global_dim_data = ({"dist_type": "b", "bounds": (0, 5)}, {"dist_type": "b", "bounds": (0, 2, 6, 7, 9)})
distribution = Distribution.from_global_dim_data(self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def load_dnpy(self, name):
"""
Load a distributed array from ``.dnpy`` files.
The ``.dnpy`` file format is a binary format inspired by NumPy's
``.npy`` format. The header of a particular ``.dnpy`` file contains
information about which portion of a DistArray is saved in it (using
the metadata outlined in the Distributed Array Protocol), and the data
portion contains the output of NumPy's `save` function for the local
array data. See the module docstring for `distarray.localapi.format` for
full details.
Parameters
----------
name : str or list of str
If a str, this is used as the prefix for the filename used by each
engine. Each engine will load a file named ``<name>_<rank>.dnpy``.
If a list of str, each engine will use the name at the index
corresponding to its rank. An exception is raised if the length of
this list is not the same as the context's communicator's size.
Returns
-------
result : DistArray
A DistArray encapsulating the file loaded on each engine.
Raises
------
TypeError
If `name` is an iterable whose length is different from the
context's communicator's size.
See Also
--------
save_dnpy : Saving files to load with with load_dnpy.
"""
def _local_load_dnpy(comm, fname_base):
from distarray.localapi import load_dnpy
fname = "%s_%s.dnpy" % (fname_base, comm.Get_rank())
local_arr = load_dnpy(comm, fname)
return proxyize(local_arr)
def _local_load_dnpy_names(comm, fnames):
from distarray.localapi import load_dnpy
fname = fnames[comm.Get_rank()]
local_arr = load_dnpy(comm, fname)
return proxyize(local_arr)
if isinstance(name, six.string_types):
func = _local_load_dnpy
elif isinstance(name, collections.Sequence):
if len(name) != len(self.targets):
errmsg = "`name` must be the same length as `self.targets`."
raise TypeError(errmsg)
func = _local_load_dnpy_names
else:
errmsg = "`name` must be a string or a list."
raise TypeError(errmsg)
da_key = self.apply(func, (self.comm, name), targets=self.targets)
return DistArray.from_localarrays(da_key[0], context=self)