def getitem(self, *args, **kwargs):
assert len(kwargs) == 1
k = kwargs.keys()[0]
v = kwargs[k]
assert len(args) == 0
if isinstance(k, collections.Callable):
return SeqUtils.expect_single([k(o) for o in self.data])
elif isinstance(k, basestring):
return SeqUtils.expect_single([o for o in self.data if getattr(o, k) == v])
else:
print "Unexpected Type of %s - %s" % (k, type(k))
assert False
def getitem(self, *args, **kwargs):
assert len(kwargs) == 1
k = kwargs.keys()[0]
v = kwargs[k]
assert len(args) == 0
if isinstance(k, collections.Callable):
return SeqUtils.expect_single([k(o) for o in self.data])
elif isinstance(k, basestring):
return SeqUtils.expect_single(
[o for o in self.data if getattr(o, k) == v])
else:
print 'Unexpected Type of %s - %s' % (k, type(k))
assert False
def get_cell(self, cellname=None):
""" Either return a cell by name if there is more than one cell, otherwise the single cell """
if cellname:
return SeqUtils.filter_expect_single(self.cells,
lambda s: s.name == cellname)
else:
return SeqUtils.expect_single(self.cells)
def get_cell(self, cellname=None):
""" Either return a cell by name if there is more than one cell, otherwise the single cell """
if cellname:
return SeqUtils.filter_expect_single(self.cells,
lambda s: s.name == cellname)
else:
return SeqUtils.expect_single(self.cells)
def buildPaneFromExistingChannelInfTau1State(existing_channel_functor, sim_config, chlname):
# Setup the channel, so we can look at inf_tau:
chl = existing_channel_functor(NEURONEnvironment())
state_name = SeqUtils.expect_single(chl.statevars.keys())
state=buildStatePane(chl, state_name)
general=HHGeneralPanel(
gbar=float(chl.conductance.rescale('mS/cm2').magnitude),
vrev=float(chl.reversalpotential.rescale("mV").magnitude)
)
return HHChannelPaneInfTau1(sim_config=sim_config,
general_pane=general,
state_pane=state,
eqn = chl.eqn,
state_var_name = state_name,
chlname = chlname
)
def load_cell_dictionaries(self, cellNode, regions=None):
l1 = Level1NeuroMLRepresentation(cellNode)
print 'CellName:', cellNode.getAttribute('name')
# We are not too worried about cables, but we do need the region name out of them:
cableIDToRegionName = {}
cablesNode = SeqUtils.filter_expect_single(cellNode.childNodes,
isElementWithTag("cables"))
for cableNode in Filter(cablesNode.childNodes,
isElementWithTag("cable")):
id = cableNode.getAttribute("id")
name = cableNode.getAttribute("name")
group_nodes = Filter(cableNode.childNodes,
isElementWithTag("group"))
if group_nodes:
if regions:
metaGroupNode = SeqUtils.filter_expect_single(
group_nodes, lambda e: get_text(e) in regions)
region_name = regions[get_text(metaGroupNode)]
else:
metaGroupNode = SeqUtils.expect_single(group_nodes)
region_name = get_text(metaGroupNode)
assert not id in cableIDToRegionName
cableIDToRegionName[id] = region_name
else:
cableIDToRegionName[id] = None
pass
print 'Loaded Cable: ', id, name
# Load the segments:
segmentListInfo = {
} # id -> (id, name, cable, parent, (px, py, pz, pDiam), (dx, dy, dz, dDiam))
segmentsNode = SeqUtils.filter_expect_single(
cellNode.childNodes, isElementWithTag("segments"))
for segNode in Filter(segmentsNode.childNodes,
isElementWithTag("segment")):
print "Segment"
id = segNode.getAttribute("id")
name = segNode.getAttribute("name")
cable = segNode.getAttribute("cable")
parent = segNode.getAttribute("parent")
# Every point should have a distal End:
dNode = SeqUtils.filter_expect_single(segNode.childNodes,
isElementWithTag("distal"))
d_x, d_y, d_z = dNode.getAttribute("x"), dNode.getAttribute(
"y"), dNode.getAttribute("z")
d_diam = dNode.getAttribute("diameter")
if not parent:
pass
pNode = SeqUtils.filter_expect_single(
segNode.childNodes, isElementWithTag("proximal"))
p_x, p_y, p_z = pNode.getAttribute("x"), pNode.getAttribute(
"y"), pNode.getAttribute("z")
p_diam = pNode.getAttribute("diameter")
else:
# Not every point need have a proximal end, we look at the parent in case it has
# both and check the locations agree
parent_Dist_Loc = segmentListInfo[parent][5]
pNode = Filter(segNode.childNodes,
isElementWithTag("proximal"))
if len(pNode) == 0:
p_x, p_y, p_z, p_diam = parent_Dist_Loc
elif len(pNode) == 1:
p_xR, p_yR, p_zR = pNode[0].getAttribute("x"), pNode[
0].getAttribute("y"), pNode[0].getAttribute("z")
p_diamR = pNode[0].getAttribute("diameter")
# I do not understand MorphML to understand why these checks do not fail....
#print (p_xR, p_yR, p_zR)
#print (parent_Dist_Loc[0], parent_Dist_Loc[1], parent_Dist_Loc[2])
#assert (p_xR, p_yR, p_zR) == (parent_Dist_Loc[0], parent_Dist_Loc[1], parent_Dist_Loc[2])
# In this case, use the diameter just read:
p_x, p_y, p_z = p_xR, p_yR, p_zR
p_diam = p_diamR
else:
assert False
print id, name, cable, parent, (p_x, p_y, p_z,
p_diam), (d_x, d_y, d_z, d_diam)
infTuple = (id, name, cable, parent, (float(p_x), float(p_y),
float(p_z), float(p_diam)),
(float(d_x), float(d_y), float(d_z), float(d_diam)))
assert not id in segmentListInfo
segmentListInfo[id] = infTuple
# Now we have read the file and created the dictionaries:
return (cableIDToRegionName, segmentListInfo)
def __init__(self, cellNode):
self.cables = SearchableSet()
self.cablegroups = SearchableSet()
self.segments = SearchableSet()
# Read the XML:
# Start with the declared Cables:
cablesNode = FilterExpectSingleChildrenByTag(cellNode, 'cables')
# Load the individual cable objects
for cableNode in FilterChildrenByTag(cablesNode, "cable"):
if cableNode.getAttribute('id') in self.cables.keys('cable_id'):
continue
cable = NeuroMLCable(cable_id=cableNode.getAttribute('id'),
name=cablesNode.getAttribute('name'))
self.cables.add(cable)
# Does the cable belong to any groups?
for group_node in Filter(cableNode.childNodes,
isElementWithTag("group")):
cablegroupname = get_text(group_node)
if not cablegroupname in self.cablegroups.keys('name'):
self.cablegroups.add(
NeuroMLCableGroup(name=cablegroupname))
self.cablegroups.getitem(name=cablegroupname).add_cable(cable)
# Load the CableGroup Objects:
for cableGroupNode in Filter(cablesNode.childNodes,
isElementWithTag("cablegroup")):
# Get, or create, the cable node:
cablegroupname = cableGroupNode.getAttribute('name')
if not cablegroupname in self.cablegroups.keys('name'):
self.cablegroups.add(NeuroMLCableGroup(name=cablegroupname))
grp = self.cablegroups.getitem(name=cablegroupname)
# Add the cable segments:
for cableNode in Filter(cableGroupNode.childNodes,
isElementWithTag("cable")):
cableNodeID = cableNode.getAttribute('id')
if not cableNodeID in self.cables.keys('cable_id'):
assert False
cable = self.cables.getitem(cable_id=cableNodeID)
grp.add_cable(cable)
# Load the Segments Objects:
segmentsNode = SeqUtils.filter_expect_single(
cellNode.childNodes, isElementWithTag("segments"))
for segmentNode in Filter(segmentsNode.childNodes,
isElementWithTag("segment")):
#Attributes:
segment_id = segmentNode.getAttribute('id')
parent_id = segmentNode.getAttribute('parent')
cable_id = segmentNode.getAttribute('cable')
cable = self.cables.getitem(
cable_id=cable_id) if cable_id else None
# Distal Information:
distalNode = FilterExpectSingleChildrenByTag(segmentNode, 'distal')
d_X, d_Y, d_Z, d_d = float(distalNode.getAttribute('x')), float(
distalNode.getAttribute('y')), float(
distalNode.getAttribute('z')), float(
distalNode.getAttribute('diameter'))
dInfo = (d_X, d_Y, d_Z, d_d)
proximalNode = FilterChildrenByTag(segmentNode, 'proximal')
# Root node:
if not parent_id:
assert proximalNode
proximalNode = SeqUtils.expect_single(proximalNode)
p_X, p_Y, p_Z, p_d = float(
proximalNode.getAttribute('x')), float(
proximalNode.getAttribute('y')), float(
proximalNode.getAttribute('z')), float(
proximalNode.getAttribute('diameter'))
pInfo = (p_X, p_Y, p_Z, p_d)
segment = NeuroMLSegment(segment_id=segment_id,
distInfo=dInfo,
proxInfo=pInfo,
parent=None,
cable=cable)
#Child Node:
else:
# Not the root node, but a proximal node is provided:
if proximalNode:
pInfo = (p_X, p_Y, p_Z, p_d)
# Proximal node and NOT the root:
if parent_id:
parentNode = self.segments.getitem(
segment_id=parent_id)
parentDistalInfo = parentNode.distInfo
eps = 0.01
# assert fabs(parentDistalInfo[0] - pInfo[0]) < eps
# assert fabs(parentDistalInfo[1] - pInfo[1]) < eps
# assert fabs(parentDistalInfo[2] - pInfo[2]) < eps
# assert fabs(parentDistalInfo[3] - pInfo[3]) < eps
# Add the child node:
segment = NeuroMLSegment(segment_id=segment_id,
distInfo=dInfo,
proxInfo=pInfo,
parent=None,
cable=cable)
# Add the segment to groups?
self.segments.add(segment)
def load_cell_dictionaries(self, cellNode, regions=None):
l1 = Level1NeuroMLRepresentation(cellNode)
print "CellName:", cellNode.getAttribute("name")
# We are not too worried about cables, but we do need the region name out of them:
cableIDToRegionName = {}
cablesNode = SeqUtils.filter_expect_single(cellNode.childNodes, isElementWithTag("cables"))
for cableNode in Filter(cablesNode.childNodes, isElementWithTag("cable")):
id = cableNode.getAttribute("id")
name = cableNode.getAttribute("name")
group_nodes = Filter(cableNode.childNodes, isElementWithTag("group"))
if group_nodes:
if regions:
# for n in group_nodes:
# print get_text(n)
metaGroupNode = SeqUtils.filter_expect_single(group_nodes, lambda e: get_text(e) in regions)
region_name = regions[get_text(metaGroupNode)]
else:
metaGroupNode = SeqUtils.expect_single(group_nodes)
region_name = get_text(metaGroupNode)
assert not id in cableIDToRegionName
cableIDToRegionName[id] = region_name
else:
cableIDToRegionName[id] = None
pass
print "Loaded Cable: ", id, name
# Load the segments:
segmentListInfo = {} # id -> (id, name, cable, parent, (px, py, pz, pDiam), (dx, dy, dz, dDiam))
segmentsNode = SeqUtils.filter_expect_single(cellNode.childNodes, isElementWithTag("segments"))
for segNode in Filter(segmentsNode.childNodes, isElementWithTag("segment")):
print "Segment"
id = segNode.getAttribute("id")
name = segNode.getAttribute("name")
cable = segNode.getAttribute("cable")
parent = segNode.getAttribute("parent")
# Every point should have a distal End:
dNode = SeqUtils.filter_expect_single(segNode.childNodes, isElementWithTag("distal"))
d_x, d_y, d_z = dNode.getAttribute("x"), dNode.getAttribute("y"), dNode.getAttribute("z")
d_diam = dNode.getAttribute("diameter")
if not parent:
pass
pNode = SeqUtils.filter_expect_single(segNode.childNodes, isElementWithTag("proximal"))
p_x, p_y, p_z = pNode.getAttribute("x"), pNode.getAttribute("y"), pNode.getAttribute("z")
p_diam = pNode.getAttribute("diameter")
else:
# Not every point need have a proximal end, we look at the parent in case it has
# both and check the locations agree
parent_Dist_Loc = segmentListInfo[parent][5]
pNode = Filter(segNode.childNodes, isElementWithTag("proximal"))
if len(pNode) == 0:
p_x, p_y, p_z, p_diam = parent_Dist_Loc
elif len(pNode) == 1:
p_xR, p_yR, p_zR = (
pNode[0].getAttribute("x"),
pNode[0].getAttribute("y"),
pNode[0].getAttribute("z"),
)
p_diamR = pNode[0].getAttribute("diameter")
# I do not understand MorphML to understand why these checks do not fail....
# print (p_xR, p_yR, p_zR)
# print (parent_Dist_Loc[0], parent_Dist_Loc[1], parent_Dist_Loc[2])
# assert (p_xR, p_yR, p_zR) == (parent_Dist_Loc[0], parent_Dist_Loc[1], parent_Dist_Loc[2])
# In this case, use the diameter just read:
p_x, p_y, p_z = p_xR, p_yR, p_zR
p_diam = p_diamR
else:
assert False
print id, name, cable, parent, (p_x, p_y, p_z, p_diam), (d_x, d_y, d_z, d_diam)
infTuple = (
id,
name,
cable,
parent,
(float(p_x), float(p_y), float(p_z), float(p_diam)),
(float(d_x), float(d_y), float(d_z), float(d_diam)),
)
assert not id in segmentListInfo
segmentListInfo[id] = infTuple
# Now we have read the file and created the dictionaries:
return (cableIDToRegionName, segmentListInfo)
def __init__(self, cellNode):
self.cables = SearchableSet()
self.cablegroups = SearchableSet()
self.segments = SearchableSet()
# Read the XML:
# Start with the declared Cables:
cablesNode = FilterExpectSingleChildrenByTag(cellNode, "cables")
# Load the individual cable objects
for cableNode in FilterChildrenByTag(cablesNode, "cable"):
if cableNode.getAttribute("id") in self.cables.keys("cable_id"):
continue
cable = NeuroMLCable(cable_id=cableNode.getAttribute("id"), name=cablesNode.getAttribute("name"))
self.cables.add(cable)
# Does the cable belong to any groups?
for group_node in Filter(cableNode.childNodes, isElementWithTag("group")):
cablegroupname = get_text(group_node)
if not cablegroupname in self.cablegroups.keys("name"):
self.cablegroups.add(NeuroMLCableGroup(name=cablegroupname))
self.cablegroups.getitem(name=cablegroupname).add_cable(cable)
# Load the CableGroup Objects:
for cableGroupNode in Filter(cablesNode.childNodes, isElementWithTag("cablegroup")):
# Get, or create, the cable node:
cablegroupname = cableGroupNode.getAttribute("name")
if not cablegroupname in self.cablegroups.keys("name"):
self.cablegroups.add(NeuroMLCableGroup(name=cablegroupname))
grp = self.cablegroups.getitem(name=cablegroupname)
# Add the cable segments:
for cableNode in Filter(cableGroupNode.childNodes, isElementWithTag("cable")):
cableNodeID = cableNode.getAttribute("id")
if not cableNodeID in self.cables.keys("cable_id"):
assert False
cable = self.cables.getitem(cable_id=cableNodeID)
grp.add_cable(cable)
# Load the Segments Objects:
segmentsNode = SeqUtils.filter_expect_single(cellNode.childNodes, isElementWithTag("segments"))
for segmentNode in Filter(segmentsNode.childNodes, isElementWithTag("segment")):
# Attributes:
segment_id = segmentNode.getAttribute("id")
parent_id = segmentNode.getAttribute("parent")
cable_id = segmentNode.getAttribute("cable")
cable = self.cables.getitem(cable_id=cable_id) if cable_id else None
# Distal Information:
distalNode = FilterExpectSingleChildrenByTag(segmentNode, "distal")
d_X, d_Y, d_Z, d_d = (
float(distalNode.getAttribute("x")),
float(distalNode.getAttribute("y")),
float(distalNode.getAttribute("z")),
float(distalNode.getAttribute("diameter")),
)
dInfo = (d_X, d_Y, d_Z, d_d)
proximalNode = FilterChildrenByTag(segmentNode, "proximal")
# Root node:
if not parent_id:
assert proximalNode
proximalNode = SeqUtils.expect_single(proximalNode)
p_X, p_Y, p_Z, p_d = (
float(proximalNode.getAttribute("x")),
float(proximalNode.getAttribute("y")),
float(proximalNode.getAttribute("z")),
float(proximalNode.getAttribute("diameter")),
)
pInfo = (p_X, p_Y, p_Z, p_d)
segment = NeuroMLSegment(
segment_id=segment_id, distInfo=dInfo, proxInfo=pInfo, parent=None, cable=cable
)
# Child Node:
else:
# Not the root node, but a proximal node is provided:
if proximalNode:
pInfo = (p_X, p_Y, p_Z, p_d)
# Proximal node and NOT the root:
if parent_id:
parentNode = self.segments.getitem(segment_id=parent_id)
parentDistalInfo = parentNode.distInfo
eps = 0.01
# assert fabs(parentDistalInfo[0] - pInfo[0]) < eps
# assert fabs(parentDistalInfo[1] - pInfo[1]) < eps
# assert fabs(parentDistalInfo[2] - pInfo[2]) < eps
# assert fabs(parentDistalInfo[3] - pInfo[3]) < eps
# Add the child node:
segment = NeuroMLSegment(
segment_id=segment_id, distInfo=dInfo, proxInfo=pInfo, parent=None, cable=cable
)
# Add the segment to groups?
self.segments.add(segment)