def test_empty_like():
z = empty(100, 10)
z2 = empty_like(z)
eq(z.shape, z2.shape)
eq(z.chunks, z2.chunks)
eq(z.dtype, z2.dtype)
eq(z.cname, z2.cname)
eq(z.clevel, z2.clevel)
eq(z.shuffle, z2.shuffle)
eq(z.fill_value, z2.fill_value)
def test_empty_like():
z = empty(100, 10)
z2 = empty_like(z)
eq(z.shape, z2.shape)
eq(z.chunks, z2.chunks)
eq(z.dtype, z2.dtype)
eq(z.cname, z2.cname)
eq(z.clevel, z2.clevel)
eq(z.shuffle, z2.shuffle)
eq(z.fill_value, z2.fill_value)
def test_set_selections_with_fields():
v = [('aaa', 1, 4.2),
('bbb', 2, 8.4),
('ccc', 3, 12.6)]
v = np.array(v, dtype=[('foo', 'S3'), ('bar', 'i4'), ('baz', 'f8')])
a = np.empty_like(v)
z = zarr.empty_like(v, chunks=2)
fields_fixture = [
'foo',
[],
['foo'],
['foo', 'bar'],
['foo', 'baz'],
['bar', 'baz'],
['foo', 'bar', 'baz'],
['bar', 'foo'],
['baz', 'bar', 'foo'],
]
for fields in fields_fixture:
# currently multi-field assignment is not supported in numpy, so we won't support
# it either
if isinstance(fields, list) and len(fields) > 1:
with pytest.raises(IndexError):
z.set_basic_selection(Ellipsis, v, fields=fields)
with pytest.raises(IndexError):
z.set_orthogonal_selection([0, 2], v, fields=fields)
with pytest.raises(IndexError):
z.set_coordinate_selection([0, 2], v, fields=fields)
with pytest.raises(IndexError):
z.set_mask_selection([True, False, True], v, fields=fields)
else:
if isinstance(fields, list) and len(fields) == 1:
# work around numpy does not support multi-field assignment even if there
# is only one field
key = fields[0]
elif isinstance(fields, list) and len(fields) == 0:
# work around numpy ambiguity about what is a field selection
key = Ellipsis
else:
key = fields
# setup expectation
a[:] = ('', 0, 0)
z[:] = ('', 0, 0)
assert_array_equal(a, z[:])
a[key] = v[key]
# total selection
z.set_basic_selection(Ellipsis, v[key], fields=fields)
assert_array_equal(a, z[:])
# basic selection with slice
a[:] = ('', 0, 0)
z[:] = ('', 0, 0)
a[key][0:2] = v[key][0:2]
z.set_basic_selection(slice(0, 2), v[key][0:2], fields=fields)
assert_array_equal(a, z[:])
# orthogonal selection
a[:] = ('', 0, 0)
z[:] = ('', 0, 0)
ix = [0, 2]
a[key][ix] = v[key][ix]
z.set_orthogonal_selection(ix, v[key][ix], fields=fields)
assert_array_equal(a, z[:])
# coordinate selection
a[:] = ('', 0, 0)
z[:] = ('', 0, 0)
ix = [0, 2]
a[key][ix] = v[key][ix]
z.set_coordinate_selection(ix, v[key][ix], fields=fields)
assert_array_equal(a, z[:])
# mask selection
a[:] = ('', 0, 0)
z[:] = ('', 0, 0)
ix = [True, False, True]
a[key][ix] = v[key][ix]
z.set_mask_selection(ix, v[key][ix], fields=fields)
assert_array_equal(a, z[:])
import sys
import timeit
import numpy as np
import line_profiler
import zarr
from zarr import blosc
sys.path.insert(0, '..')
# setup
a = np.random.normal(2000, 1000, size=200000000).astype('u2')
z = zarr.empty_like(a,
chunks=1000000,
compression='blosc',
compression_opts=dict(cname='lz4', clevel=5, shuffle=2))
print(z)
print('*' * 79)
# time
t = timeit.repeat('z[:] = a', repeat=10, number=1, globals=globals())
print(t)
print(min(t))
print(z)
# profile
profile = line_profiler.LineProfiler(blosc.compress)
profile.run('z[:] = a')
profile.print_stats()
