async def test_array():
t = ts.array(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64))
assert t.spec().to_json(include_defaults=False) == {
"driver": "array",
"array": [[1, 2, 3], [4, 5, 6]],
"dtype": "int64",
"transform": {
"input_inclusive_min": [0, 0],
"input_exclusive_max": [2, 3]
},
}
assert t.T.spec().to_json(include_defaults=False) == {
"driver": "array",
"array": [[1, 4], [2, 5], [3, 6]],
"dtype": "int64",
"transform": {
"input_inclusive_min": [0, 0],
"input_exclusive_max": [3, 2],
},
}
assert t[0].spec().to_json(include_defaults=False) == {
"driver": "array",
"array": [1, 2, 3],
"dtype": "int64",
"transform": {
"input_inclusive_min": [0],
"input_exclusive_max": [3]
},
}
async def test_downsample_store_uint32():
t = ts.array(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint32))
downsampled = ts.downsample(t, [2, 2], method='mean')
np.testing.assert_equal(
np.array([[3, 4]], dtype=np.uint32), await
downsampled.read())
async def test_downsample_spec():
t = ts.array(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32))
spec = t.spec()
downsampled_spec = ts.downsample(spec, [1, 2], method='mean')
downsampled = await ts.open(downsampled_spec)
np.testing.assert_equal(
np.array([[1.5, 3], [4.5, 6]], dtype=np.float32), await
downsampled.read())
async def test_read_adapt():
"""Tests reading where the `read_function` itself reads from a TensorStore."""
a = ts.array([[1, 2, 3], [4, 5, 6]], dtype=ts.int32)
async def do_read(domain, array, read_params):
del read_params
array[...] = (await a[domain].read()) + 100
t = ts.virtual_chunked(do_read, dtype=a.dtype, domain=a.domain)
np.testing.assert_array_equal(
await t.read(),
np.array([[101, 102, 103], [104, 105, 106]], dtype=np.int32))
),
3,
),
(
Tracks,
np.column_stack(
(np.ones(20), np.arange(20), 20 * np.random.random((20, 3)))
),
4,
),
]
try:
import tensorstore as ts
m = ts.array(np.random.random((10, 15)))
p = [ts.array(np.random.random(s)) for s in [(40, 20), (20, 10), (10, 5)]]
layer_test_data.extend([(Image, m, 2), (Image, p, 2)])
except ImportError:
pass
classes = [Labels, Points, Vectors, Shapes, Surface, Tracks, Image]
names = [cls.__name__.lower() for cls in classes]
layer2addmethod = {
cls: getattr(Viewer, 'add_' + name) for cls, name in zip(classes, names)
}
# examples of valid tuples that might be passed to viewer._add_layer_from_data
good_layer_data = [
(np.random.random((10, 10)),),
def test_normalize_dtype_tensorstore(dtype_str):
np_arr = np.zeros(5, dtype=dtype_str)
ts_arr = ts.array(np_arr) # inherit ts dtype from np dtype
assert normalize_dtype(ts_arr.dtype) is np_arr.dtype.type
async def test_cast(): t = ts.array(np.array([0, 1, 2, 3], dtype=np.int64)) t_string = t.astype(bytes) np.testing.assert_equal(await t_string.read(), [b"0", b"1", b"2", b"3"]) t_bool = ts.cast(t, bool) np.testing.assert_equal(await t_bool.read(), [False, True, True, True])
