def _unchunk(v):
#idx, data = zip(*v.data)
idx, data = v
arr = empty(nchunks, dtype='object')
for (i, d) in zip(idx, data):
arr[i] = d
return allstack(arr.tolist())
def _unchunk(v):
#idx, data = zip(*v.data)
idx, data = v
arr = empty(nchunks, dtype='object')
for (i, d) in zip(idx, data):
arr[i] = d
return allstack(arr.tolist())
def _unchunk(it):
ordered = sorted(it, key=lambda kv: kv[0][split:])
keys, values = zip(*ordered)
k_chks = [k[split:] for k in keys]
arr = empty(nchunks, dtype='object')
for (i, d) in zip(k_chks, values):
arr[i] = d
yield keys[0][:split], allstack(arr.tolist())
def _unchunk(it):
ordered = sorted(it, key=lambda kv: kv[0][split:])
keys, values = zip(*ordered)
k_chks = [k[split:] for k in keys]
arr = empty(nchunks, dtype='object')
for (i, d) in zip(k_chks, values):
arr[i] = d
yield keys[0][:split], allstack(arr.tolist())
def unchunk(self):
"""
Reconstitute the chunked array back into a full ndarray.
Returns
-------
ndarray
"""
if self.padding != len(self.shape)*(0,):
shape = self.values.shape
arr = empty(shape, dtype=object)
for inds in product(*[arange(s) for s in shape]):
slices = []
for i, p, n in zip(inds, self.padding, shape):
start = None if (i == 0 or p == 0) else p
stop = None if (i == n-1 or p == 0) else -p
slices.append(slice(start, stop, None))
arr[inds] = self.values[inds][tuple(slices)]
else:
arr = self.values
return allstack(arr.tolist())
def unblock(self):
"""
Reconstitute the blocked array back into a full ndarray
Returns
-------
ndarray
"""
if self.padding is not None:
shape = self.values.shape
arr = empty(shape, dtype=object)
for inds in product([arange(s) for s in shape]):
slices = []
for i, p, n in zip(inds, self.padding, shape):
start = None if i == 0 else p
stop = None if i == n else -p
slices += slice(start, stop, None)
arr[inds] = arr[inds][slices]
else:
arr = self.values
return allstack(self.values.tolist())