def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
return self._synchronized_op(f, *args, **kwargs)
def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
return self._synchronized_op(f, *args, **kwargs)
def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
# synchronization
if self._synchronizer is None:
return f(*args, **kwargs)
else:
# synchronize on the root group
with self._synchronizer[group_meta_key]:
return f(*args, **kwargs)
def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
# synchronization
if self._synchronizer is None:
return f(*args, **kwargs)
else:
# synchronize on the root group
with self._synchronizer[group_meta_key]:
return f(*args, **kwargs)
def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
if self._synchronizer is None:
# no synchronization
lock = nolock
else:
# synchronize on the root group
lock = self._synchronizer[group_meta_key]
with lock:
return f(*args, **kwargs)
def _write_op(self, f, *args, **kwargs):
# guard condition
if self._read_only:
err_read_only()
if self._synchronizer is None:
# no synchronization
lock = nolock
else:
# synchronize on the root group
lock = self._synchronizer[group_meta_key]
with lock:
return f(*args, **kwargs)
def __setitem__(self, item, value):
"""Modify data for some portion of the array.
Examples
--------
Setup a 1-dimensional array::
>>> import zarr
>>> z = zarr.zeros(100000000, chunks=1000000, dtype='i4')
>>> z
Array((100000000,), int32, chunks=(1000000,), order=C)
nbytes: 381.5M; nbytes_stored: ...; ratio: ...; initialized: 0/100
compressor: Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
store: dict
Set all array elements to the same scalar value::
>>> z[:] = 42
>>> z[:]
array([42, 42, 42, ..., 42, 42, 42], dtype=int32)
Set a portion of the array::
>>> z[:100] = np.arange(100)
>>> z[-100:] = np.arange(100)[::-1]
>>> z[:]
array([0, 1, 2, ..., 2, 1, 0], dtype=int32)
Setup a 2-dimensional array::
>>> z = zarr.zeros((10000, 10000), chunks=(1000, 1000), dtype='i4')
>>> z
Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
nbytes: 381.5M; nbytes_stored: ...; ratio: ...; initialized: 0/100
compressor: Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
store: dict
Set all array elements to the same scalar value::
>>> z[:] = 42
>>> z[:]
array([[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
...,
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42]], dtype=int32)
Set a portion of the array::
>>> z[0, :] = np.arange(z.shape[1])
>>> z[:, 0] = np.arange(z.shape[0])
>>> z[:]
array([[ 0, 1, 2, ..., 9997, 9998, 9999],
[ 1, 42, 42, ..., 42, 42, 42],
[ 2, 42, 42, ..., 42, 42, 42],
...,
[9997, 42, 42, ..., 42, 42, 42],
[9998, 42, 42, ..., 42, 42, 42],
[9999, 42, 42, ..., 42, 42, 42]], dtype=int32)
"""
# guard conditions
if self._read_only:
err_read_only()
# refresh metadata
if not self._cache_metadata:
self._load_metadata_nosync()
# normalize selection
selection = normalize_array_selection(item, self._shape)
# check value shape
expected_shape = tuple(s.stop - s.start for s in selection
if isinstance(s, slice))
if np.isscalar(value):
pass
elif expected_shape != value.shape:
raise ValueError('value has wrong shape, expecting %s, found %s' %
(str(expected_shape), str(value.shape)))
# determine indices of chunks overlapping the selection
chunk_range = get_chunk_range(selection, self._chunks)
# iterate over chunks in range
for cidx in itertools.product(*chunk_range):
# determine chunk offset
offset = [i * c for i, c in zip(cidx, self._chunks)]
# determine required index range within chunk
chunk_selection = tuple(
slice(max(0, s.start -
o), min(c, s.stop -
o)) if isinstance(s, slice) else s - o
for s, o, c in zip(selection, offset, self._chunks))
if np.isscalar(value):
# put data
self._chunk_setitem(cidx, chunk_selection, value)
else:
# assume value is array-like
# determine index within value
value_selection = tuple(
slice(max(0, o -
s.start), min(o + c - s.start, s.stop - s.start))
for s, o, c in zip(selection, offset, self._chunks)
if isinstance(s, slice))
# put data
self._chunk_setitem(cidx, chunk_selection,
value[value_selection])
def __setitem__(self, key, value):
if self.mode == 'r':
err_read_only()
value = ensure_bytes(value)
self.zf.writestr(key, value)
def __setitem__(self, key, value):
if self.mode == 'r':
err_read_only()
value = ensure_bytes(value)
self.zf.writestr(key, value)
def __setitem__(self, item, value):
"""Modify data for some portion of the array.
Examples
--------
Setup a 1-dimensional array::
>>> import zarr
>>> z = zarr.zeros(100000000, chunks=1000000, dtype='i4')
>>> z
Array((100000000,), int32, chunks=(1000000,), order=C)
nbytes: 381.5M; nbytes_stored: 301; ratio: 1328903.7; initialized: 0/100
compressor: Blosc(cname='lz4', clevel=5, shuffle=1)
store: dict
Set all array elements to the same scalar value::
>>> z[:] = 42
>>> z[:]
array([42, 42, 42, ..., 42, 42, 42], dtype=int32)
Set a portion of the array::
>>> z[:100] = np.arange(100)
>>> z[-100:] = np.arange(100)[::-1]
>>> z[:]
array([0, 1, 2, ..., 2, 1, 0], dtype=int32)
Setup a 2-dimensional array::
>>> z = zarr.zeros((10000, 10000), chunks=(1000, 1000), dtype='i4')
>>> z
Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
nbytes: 381.5M; nbytes_stored: 323; ratio: 1238390.1; initialized: 0/100
compressor: Blosc(cname='lz4', clevel=5, shuffle=1)
store: dict
Set all array elements to the same scalar value::
>>> z[:] = 42
>>> z[:]
array([[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
...,
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42],
[42, 42, 42, ..., 42, 42, 42]], dtype=int32)
Set a portion of the array::
>>> z[0, :] = np.arange(z.shape[1])
>>> z[:, 0] = np.arange(z.shape[0])
>>> z[:]
array([[ 0, 1, 2, ..., 9997, 9998, 9999],
[ 1, 42, 42, ..., 42, 42, 42],
[ 2, 42, 42, ..., 42, 42, 42],
...,
[9997, 42, 42, ..., 42, 42, 42],
[9998, 42, 42, ..., 42, 42, 42],
[9999, 42, 42, ..., 42, 42, 42]], dtype=int32)
"""
# guard conditions
if self._read_only:
err_read_only()
# refresh metadata
if not self._cache_metadata:
self._load_metadata_nosync()
# normalize selection
selection = normalize_array_selection(item, self._shape)
# check value shape
expected_shape = tuple(
s.stop - s.start for s in selection
if isinstance(s, slice)
)
if np.isscalar(value):
pass
elif expected_shape != value.shape:
raise ValueError('value has wrong shape, expecting %s, found %s'
% (str(expected_shape),
str(value.shape)))
# determine indices of chunks overlapping the selection
chunk_range = get_chunk_range(selection, self._chunks)
# iterate over chunks in range
for cidx in itertools.product(*chunk_range):
# determine chunk offset
offset = [i * c for i, c in zip(cidx, self._chunks)]
# determine required index range within chunk
chunk_selection = tuple(
slice(max(0, s.start - o), min(c, s.stop - o))
if isinstance(s, slice)
else s - o
for s, o, c in zip(selection, offset, self._chunks)
)
if np.isscalar(value):
# put data
self._chunk_setitem(cidx, chunk_selection, value)
else:
# assume value is array-like
# determine index within value
value_selection = tuple(
slice(max(0, o - s.start),
min(o + c - s.start, s.stop - s.start))
for s, o, c in zip(selection, offset, self._chunks)
if isinstance(s, slice)
)
# put data
self._chunk_setitem(cidx, chunk_selection,
value[value_selection])
