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(self):
mc0 = self.simulation.gids('MiniColumn_2')
mc1 = self.simulation.gids('MiniColumn_5')
mc2 = self.simulation.gids('MiniColumn_8')
target = numpy.union1d(numpy.union1d(mc0, mc1), mc2)
circuit = self.simulation.open_circuit()
synapses = circuit.afferent_synapses(target)
# Reference image with two columns
attributes = AttributeMap()
attributes.mode = RepresentationMode.SOMA
attributes.color = [1, 1, 1, 1]
self.scene.addNeurons(mc0, attributes)
attributes.color = [1, 0, 0, 1]
self.scene.addNeurons(mc1, attributes)
self.tmp_images.append(image_compare.capture_temporary(self.view))
self.scene.clear()
self.engine.frame()
# Reference image with three columns and synapses
attributes = AttributeMap()
attributes.mode = RepresentationMode.SOMA
attributes.color = [1, 1, 1, 1]
self.scene.addNeurons(mc0, attributes)
attributes.color = [1, 0, 0, 1]
self.scene.addNeurons(mc1, attributes)
attributes.color = [0, 0, 1, 1]
self.scene.addNeurons(mc2, attributes)
self.scene.addAfferentSynapses(synapses)
self.tmp_images.append(image_compare.capture_temporary(self.view))
self.scene.clear()
self.engine.frame()
# Now do the same with incremental loads
attributes.color = [1, 1, 1, 1]
self.scene.addNeurons(mc0, attributes)
self.engine.frame()
attributes.color = [1, 0, 0, 1]
self.scene.addNeurons(mc1, attributes)
self.engine.frame()
self.compare_with(0)
attributes.color = [0, 0, 1, 1]
handler1 = self.scene.addNeurons(mc2, attributes)
self.engine.frame()
handler2 = self.scene.addAfferentSynapses(synapses)
self.engine.frame()
self.compare_with(1)
# Compare after removing a neuron and a synapse target
self.scene.remove(handler1)
self.engine.frame()
self.scene.remove(handler2)
self.engine.frame()
self.compare_with(0)
def _remove_colormaps_test(self, spikes=False):
if self.tmp:
os.remove(self.tmp)
rtneuron.engine.player.timestamp = 5
# Remove from same handler
self.tmp = image_compare.capture_temporary(self.view)
subsets, colormaps = self._apply_colormaps(spikes)
for subset in subsets:
subset.attributes.colormaps = AttributeMap()
subset.update()
image_compare.capture_and_compare(self.view,
self.tmp,
prepend_sample_path=False)
# Remove from top handler
subsets, colormaps = self._apply_colormaps(spikes)
handler = self.view.scene.objects[0]
handler.attributes.colormaps = AttributeMap()
handler.update()
image_compare.capture_and_compare(self.view,
self.tmp,
prepend_sample_path=False)
self._clear_colormaps(subsets) # To avoid warning
# Remove from an overlapping subhandler
subsets, colormaps = self._apply_colormaps(spikes)
subset = handler.query(self.gids)
subset.attributes.colormaps = AttributeMap()
subset.update()
image_compare.capture_and_compare(self.view,
self.tmp,
prepend_sample_path=False)
self._clear_colormaps(subsets) # To avoid warning
def test_axon_mode_protected(self):
tmp = image_compare.capture_temporary(self.view)
self._handler.apply(NeuronClipping().unclipAll())
try:
image_compare.capture_and_compare(
self.view, tmp, prepend_sample_path = False)
finally:
os.remove(tmp)
def test_add_remove(self):
handler = self.scene.addModel(cube_path)
tmp = image_compare.capture_temporary(self.view)
try:
self.scene.remove(handler)
image_compare.capture_and_compare(self.view, 'empty.png', 1, tmp)
except:
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_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_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_mode_to_no_display(self, mode):
tmp = image_compare.capture_temporary(self._view)
self._scene.addNeurons(self._gids, AttributeMap({'mode': mode}))
for i in range(2):
self._engine.frame()
self._engine.waitFrame()
obj = self._scene.objects[0]
obj.attributes.mode = RepresentationMode.NO_DISPLAY
obj.update()
try:
image_compare.capture_and_compare(self._view,
tmp,
prepend_sample_path=False)
finally:
os.remove(tmp)
def test_apply_colormaps(self):
rtneuron.engine.player.timestamp = 5.5
self.tmp = image_compare.capture_temporary(self.view)
subsets, colormaps = self._apply_colormaps()
image_compare.capture_and_compare(
self.view, 'compartments_with_colormaps_on_soma.png')
colormaps[0].setRange(-80, -60)
colormaps[1].setRange(-70, -60)
colormaps[2].setRange(-70, -50)
for s in subsets:
s.update()
image_compare.capture_and_compare(
self.view, 'compartments_with_colormaps_on_soma_2.png')
self._clear_colormaps(subsets) # To avoid warning
subsets = self._set_default_colormap_at_subsets()
image_compare.capture_and_compare(self.view,
self.tmp,
prepend_sample_path=False)
self._clear_colormaps(subsets) # To avoid warning
def test_with_subset_modifications(self):
simulation = self.simulation
mc0 = simulation.gids('MiniColumn_2')
mc1 = simulation.gids('MiniColumn_5')
gids = numpy.append(mc0, mc1)
circuit = self.simulation.open_circuit()
synapses = circuit.afferent_synapses(numpy.union1d(mc0, mc1))
# Reference image
attributes = AttributeMap()
attributes.mode = RepresentationMode.SOMA
attributes.color = [1, 1, 1, 1]
handler = self.scene.addNeurons(gids, attributes)
self.scene.addAfferentSynapses(synapses)
self.engine.frame()
subset = handler.query(mc0)
subset.attributes.color = [1, 0, 1, 1]
subset.update()
self.tmp_images.append(image_compare.capture_temporary(self.view))
self.scene.clear()
self.engine.frame()
# And now the same but doing the subset modification before adding
# the synapses.
attributes = AttributeMap()
attributes.mode = RepresentationMode.SOMA
attributes.color = [1, 1, 1, 1]
handler = self.scene.addNeurons(gids, attributes)
subset = handler.query(mc0)
subset.attributes.color = [1, 0, 1, 1]
subset.update()
self.engine.frame()
self.scene.addAfferentSynapses(synapses)
self.compare_with(0)
def compare_with(self, index):
try:
tmp = image_compare.capture_temporary(self.view)
image_compare.compare(tmp, self.tmp_images[index])
finally:
os.remove(tmp)
