def setUp(self):
import numpy as np
from datasources import ArraySource
self.raw = np.random.randint(0, 100, (10, 3, 3, 128, 3))
self.a = ArraySource(self.raw)
self.ss = SliceSource(self.a, projectionAlongTZC)
def createDataSource(dset, withShape=False):
dset_5d = H5pyDset5DWrapper(dset)
src = ArraySource(dset_5d)
if withShape:
return src, dset_5d.shape
else:
return src
class SliceSourceTest(ut.TestCase):
def setUp(self):
import numpy as np
from datasources import ArraySource
self.raw = np.random.randint(0, 100, (10, 3, 3, 128, 3))
self.a = ArraySource(self.raw)
self.ss = SliceSource(self.a, projectionAlongTZC)
def testRequest(self):
self.ss.setThrough(0, 1)
self.ss.setThrough(2, 2)
self.ss.setThrough(1, 127)
sl = self.ss.request((slice(None), slice(None))).wait()
self.assertTrue(np.all(sl == self.raw[1, :, :, 127, 2]))
sl_bounded = self.ss.request((slice(0, 3), slice(1, None))).wait()
self.assertTrue(np.all(sl_bounded == self.raw[1, 0:3, 1:, 127, 2]))
def testDirtynessPropagation(self):
self.ss.setThrough(0, 1)
self.ss.setThrough(2, 2)
self.ss.setThrough(1, 127)
self.triggered = False
def check1(dirty_area):
self.triggered = True
self.assertEqual(dirty_area, sl[:, 1:2])
self.ss.isDirty.connect(check1)
self.a.setDirty(sl[1:2, :, 1:2, 127:128, 2:3])
self.ss.isDirty.disconnect(check1)
self.assertTrue(self.triggered)
del self.triggered
def check2(dirty_area):
assert False
self.ss.isDirty.connect(check2)
self.a.setDirty(sl[1:2, :, 1:2, 127:128, 3:4])
self.ss.isDirty.disconnect(check2)
class SliceSourceTest(ut.TestCase):
def setUp(self):
import numpy as np
from datasources import ArraySource
self.raw = np.random.randint(0, 100, (10, 3, 3, 128, 3))
self.a = ArraySource(self.raw)
self.ss = SliceSource(self.a, projectionAlongTZC)
def testRequest(self):
self.ss.setThrough(0, 1)
self.ss.setThrough(2, 2)
self.ss.setThrough(1, 127)
sl = self.ss.request((slice(None), slice(None))).wait()
self.assertTrue(np.all(sl == self.raw[1, :, :, 127, 2]))
sl_bounded = self.ss.request((slice(0, 3), slice(1, None))).wait()
self.assertTrue(np.all(sl_bounded == self.raw[1, 0:3, 1:, 127, 2]))
def testDirtynessPropagation(self):
self.ss.setThrough(0, 1)
self.ss.setThrough(2, 2)
self.ss.setThrough(1, 127)
self.triggered = False
def check1(dirty_area):
self.triggered = True
self.assertEqual(dirty_area, sl[:, 1:2])
self.ss.isDirty.connect(check1)
self.a.setDirty(sl[1:2, :, 1:2, 127:128, 2:3])
self.ss.isDirty.disconnect(check1)
self.assertTrue(self.triggered)
del self.triggered
def check2(dirty_area):
assert False
self.ss.isDirty.connect(check2)
self.a.setDirty(sl[1:2, :, 1:2, 127:128, 3:4])
self.ss.isDirty.disconnect(check2)
def _createArrayDataSource(source,withShape = False):
#has to handle NumpyArray
#check if the array is 5d, if not so embed it in a canonical way
if len(source.shape) == 2:
source = source.reshape( (1,) + source.shape + (1,1) )
elif len(source.shape) == 3 and source.shape[2] <= 4:
source = source.reshape( (1,) + source.shape[0:2] + (1,) + (source.shape[2],) )
elif len(source.shape) == 3:
source = source.reshape( (1,) + source.shape + (1,) )
elif len(source.shape) == 4:
source = source.reshape( (1,) + source.shape )
src = ArraySource(source)
if withShape:
return src,source.shape
else:
return src
def setUp(self):
import numpy as np
from datasources import ArraySource
self.raw = np.random.randint(0, 100, (10, 3, 3, 128, 3))
self.a = ArraySource(self.raw)
self.ss = SliceSource(self.a, projectionAlongTZC)
import dask.array
from datasources import ArraySource
from in_memory_catalog import Catalog
# Build Catalog of Catalogs.
subcatalogs = {}
for name, size, fruit, animal in zip(
["tiny", "small", "medium", "large"],
[3, 100, 1000, 10_000],
["apple", "banana", "orange", "grape"],
["bird", "cat", "dog", "penguin"],
):
subcatalogs[name] = Catalog(
{
k: ArraySource(v * dask.array.ones((size, size)))
for k, v in zip(["ones", "twos", "threes"], [1, 2, 3])
},
metadata={
"fruit": fruit,
"animal": animal
},
)
catalog = Catalog(subcatalogs)
