def test_unclip(self):
self._handler.apply(NeuronClipping().clipAll(True))
self._handler.apply(NeuronClipping().unclip(
range(1, 501), [0.5] * 500, [1.0] * 500))
# This result must match the result from test_clip
image_compare.capture_and_compare(
self.view, self._prefix + "simple_clip.png")
def test_multiple_clip_1(self):
clipping = NeuronClipping()
for i in range(500):
clipping.clip([i], [0.0], [0.5])
self._handler.apply(clipping)
image_compare.capture_and_compare(
self.view, self._prefix + "simple_clip.png")
def test_soma_mode(self):
self._handler.attributes.mode = RepresentationMode.SOMA
self._handler.update()
self.engine.frame()
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(NeuronClipping().clipAll(False))
try:
image_compare.capture_and_compare(self.view,
tmp,
prepend_sample_path=False)
finally:
os.remove(tmp)
self._handler.attributes.mode = RepresentationMode.NO_DISPLAY
self._handler.update()
self.engine.frame()
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(NeuronClipping().clipAll(True))
try:
image_compare.capture_and_compare(self.view,
tmp,
prepend_sample_path=False)
finally:
os.remove(tmp)
def test_multiple_unclip_2(self):
self._handler.apply(NeuronClipping().clipAll(True))
clipping = NeuronClipping()
for i in range(1, 501):
self._handler.apply(NeuronClipping().unclip([i], [0.5], [1.0]))
self._handler.apply(clipping)
image_compare.capture_and_compare(
self.view, self._prefix + "simple_clip.png")
def test_unclip_all(self):
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(NeuronClipping().clipAll(True))
self._handler.apply(NeuronClipping().unclipAll())
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_clip_and_unclip_are_perfect_opposites(self):
from random import random
starts = [random() for i in range(500)]
ends = [s + random() * (1 - s) for s in starts]
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(NeuronClipping().clip(range(500), starts, ends))
self._handler.apply(NeuronClipping().unclip(range(500), starts, ends))
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_clip_soma_interval(self):
# Soma is clipping with any range
self._handler.apply(NeuronClipping().clipAll())
self._handler.apply(NeuronClipping().clip([0], [0], [0.1]))
tmp = image_compare.capture_temporary(self.view)
self._handler.attributes.mode = RepresentationMode.NO_DISPLAY
self._handler.update()
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_clip_chain_1(self):
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(
NeuronClipping().clipAll()
.unclip(range(500), [0] * 500, [1.0] * 500))
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_clip(self):
self._handler.apply(NeuronClipping().clip(
range(500), [0.0] * 500, [0.5] * 500))
# Rendering at least one frame to make sure the scene is not empty for
# the snapshot.
self.engine.frame()
self.engine.waitFrame()
image_compare.capture_and_compare(
self.view, self._prefix + "simple_clip.png")
def test_clip(self):
self._handler.apply(NeuronClipping().clip(range(500), [0.0] * 500,
[0.5] * 500))
# Displaying the first frame to ensure that clipping operations
# have the intended effect
for i in range(2):
self.engine.frame()
self.engine.waitFrame()
image_compare.capture_and_compare(self.view,
self._prefix + "simple_clip.png")
def test_badlengths(self):
clipping = NeuronClipping()
self.assertRaises(ValueError, NeuronClipping.clip, clipping,
[0], [0], [0, 1])
self.assertRaises(ValueError, NeuronClipping.clip, clipping,
[0], [0, 1], [0])
self.assertRaises(ValueError, NeuronClipping.clip, clipping,
[0, 1], [0], [0])
self.assertRaises(ValueError, NeuronClipping.unclip, clipping,
[0], [0], [0, 1])
self.assertRaises(ValueError, NeuronClipping.unclip, clipping,
[0], [0, 1], [0])
self.assertRaises(ValueError, NeuronClipping.unclip, clipping,
[0, 1], [0], [0])
def test_clip_axon(self):
sections = [int(x) for x in self._morphology.section_ids(
[brain.neuron.SectionType.axon])]
length = len(sections)
self._handler.apply(NeuronClipping().clip(
sections, [0] * length, [1] * length))
tmp = image_compare.capture_temporary(self.view)
self._handler.attributes.mode = RepresentationMode.NO_AXON
self._handler.update()
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_clip_chain_2(self):
self._handler.apply(
NeuronClipping().clip(range(500), [0] * 500, [0.75] * 500)
.unclip(range(1, 501), [0.5] * 500, [0.75] * 500))
image_compare.capture_and_compare(
self.view, self._prefix + "simple_clip.png")