def _encodeSynSparse(self,
synIds,
weights,
cIdxOffset,
cIdxMult,
signMode,
kernelIds=None,
delays=None,
softReset=False):
"""Encode synapses using SPARSE compression.
:param np.ndarray synIds: Synapse indices.
:param np.ndarray weights: Weight values.
:param int cIdxOffset: cIdxOffset.
:param int cIdxMult: cIdxMult.
:param int signMode: Defines how inhibitory and excitatory weights are
treated. 1: Signs are not shared. 2: Excitatory connections with
shared sign. 3: Inhibitory connections with shared sign.
:param np.ndarray kernelIds: Global indices of synapse weights. Only
needed for plotting and reconstructing kernelIdMap to validate
partition.
:param np.ndarray delays: Delay values
:param bool softReset: True if synapse is used in soft-reset mode.
:return: List of synaptic entries.
:rtype: list[SynEntry]
"""
kIds = None
dlys = None
numSyn = len(synIds)
numSynEntries = int(math.ceil(numSyn / self._maxNumSynPerSynEntry))
for i in range(numSynEntries):
# Compute id range of up to 60 synapses of current synEntry
cIdStart = i * self._maxNumSynPerSynEntry
cIdEnd = min(cIdStart + self._maxNumSynPerSynEntry, numSyn)
# Get synaptic configuration
idx = synIds[cIdStart:cIdEnd]
if kernelIds is not None:
kIds = kernelIds[cIdStart:cIdEnd]
wgts = weights[cIdStart:cIdEnd]
if delays is not None:
dlys = delays[cIdStart:cIdEnd]
numIdxBits = self._getNumIdxBits(np.max(idx))
# Generate synapses and synEntries
synFmt = SynFmt(len(self._synFmts), cIdxOffset, cIdxMult,
numIdxBits, 0, self.numWeightBits,
Compression.SPARSE, signMode, self.numDelayBits,
softReset)
self._synFmts.append(synFmt)
synEntry = SynEntry(0, idx, wgts, synFmt, kIds, dlys)
self._synEntries.append(synEntry)
def compressSynFmts(synFmts, maxNumSynFmt):
"""Compress ``synFmts`` by pruning and merging.
Finds the unique set of synFmts and merges the remaining synFmts until the
number of different synFmts is below ``maxNumSynFmts``.
:param list[SynFmt] synFmts: List of all synFmt objects.
:param int maxNumSynFmt: Maximum number of synFmts supported by core.
:returns: tuple(synFmts, synEntryToFmtMap)
synFmts is a list of merged synFmt objects.
synEntryToFmtMap is a mapping vector from global synEntry
index to set of merged synFmts.
"""
# Extract synFmt properties into array for processing
synFmtProperties = np.stack([synFmt.asArray() for synFmt in synFmts])
# Extract unique synFmts properties and mapping from synEntries to synFmts
synFmtProperties, synEntryToFmtMap = np.unique(synFmtProperties,
return_inverse=True,
axis=0)
numSynFmts = synFmtProperties.shape[0]
# Compute pairwise distances between rows of synFmtProperties
# (cIdx{Offset/Mult} and compression receive high weight because they are
# not allowed to differ). It's conceivable to merge different compressions
# but that requires re-encoding of synEntries.
distances = np.infty * np.ones((numSynFmts, numSynFmts))
for i in range(numSynFmts):
for j in range(i):
distances[i, j] = _computeDistance(synFmtProperties[i],
synFmtProperties[j])
# Merge closest rows of synFmtProperties iteratively until number of
# distinct synFmts is small enough.
while numSynFmts > maxNumSynFmt:
if np.all(np.isinf(distances)):
print(numSynFmts)
break
# Find index of shortest distance in distances.
idx = np.unravel_index([np.argmin(distances)], distances.shape)
idx0, idx1 = sorted([idx[0][0], idx[1][0]])
# Overwrite the first of the two closest rows with the merged row.
# The merged row has the same cIdx{Offset/Mult} and compression but
# uses their max of numIdxBits, numSkipBits and numWgtBits.
synFmtProperties[idx0, 2:5] = \
np.max(synFmtProperties[[idx0, idx1], 2:5], axis=0)
# Delete the second of the two closest rows in synFmtProperties and in
# distances.
synFmtProperties = np.delete(synFmtProperties, idx1, axis=0)
distances = np.delete(np.delete(distances, idx1, axis=0), idx1, axis=1)
# Recompute distances of all other rows to merged row.
for j in range(idx0):
distances[idx0, j] = _computeDistance(synFmtProperties[idx0],
synFmtProperties[j])
for j in range(idx0 + 1, distances.shape[1]):
distances[j, idx0] = _computeDistance(synFmtProperties[j],
synFmtProperties[idx0])
# Remap synEntryToFmtMap from redundant row to merged row.
synEntryToFmtMap[synEntryToFmtMap == idx1] = idx0
# Reduce all row indices above the redundant row by one.
synEntryToFmtMap[synEntryToFmtMap > idx1] -= 1
numSynFmts -= 1
synFmts = []
for i in range(numSynFmts):
args = synFmtProperties[i, :-4]
# Convert compression arg from int to Enum.
kwargs = {
'compression': Compression(synFmtProperties[i, -4]),
'signMode': synFmtProperties[i, -3],
'numDlyBits': synFmtProperties[i, -2],
'softReset': synFmtProperties[i, -1]
}
synFmts.append(SynFmt(i, *args, **kwargs))
return synFmts, synEntryToFmtMap
def _encodeSynDense2(self,
synIds,
weights,
cIdxOffset,
cIdxMult,
signMode,
kernelIds=None,
delays=None,
softReset=False):
"""Encode synapses using DENSE compression.
This encoder does not create synaptic entries whenever the
compartment index increments by more than 1. Instead it inserts dummy
connections with weight 0.
:param np.ndarray synIds: Synapse indices.
:param np.ndarray weights: Weight values.
:param int cIdxOffset: cIdxOffset.
:param int cIdxMult: cIdxMult.
:param int signMode: Defines how inhibitory and excitatory weights are
treated. 1: Signs are not shared. 2: Excitatory connections with
shared sign. 3: Inhibitory connections with shared sign.
:param np.ndarray kernelIds: Global indices of synapse weights. Only
needed for plotting and reconstructing kernelIdMap to validate
partition.
:param np.ndarray delays: Delay values
:param bool softReset: True if synapse is used in soft-reset mode.
:return: List of synaptic entries.
:rtype: list[SynEntry]
"""
cIdsUnprocessed = np.copy(synIds)
wgtsUnprocessed = np.copy(weights)
dlysUnprocessed = None if delays is None else np.copy(delays)
kIdsUnprocessed = None if kernelIds is None else np.copy(kernelIds)
kIds = None
dlys = None
while len(cIdsUnprocessed):
# Extract compartment index range of at most size maxNumSynPerEntry
cIdStart = cIdsUnprocessed[0]
mask = cIdsUnprocessed < (cIdStart + self._maxNumSynPerSynEntry)
notMask = np.logical_not(mask)
cIds = cIdsUnprocessed[mask]
cIdEnd = np.max(cIds) + 1
# Get synaptic configuration
idx = np.arange(cIdEnd - cIdStart)
prefixOffset = cIdStart
if kernelIds is not None:
kIds = np.zeros(idx.shape, int)
kIds[cIds - cIdStart] = kIdsUnprocessed[mask]
kIdsUnprocessed = kIdsUnprocessed[notMask]
wgts = np.zeros(idx.shape, int)
wgts[cIds - cIdStart] = wgtsUnprocessed[mask]
if delays is not None:
dlys = np.zeros(idx.shape, int)
dlys[cIds - cIdStart] = dlysUnprocessed[mask]
dlysUnprocessed = dlysUnprocessed[notMask]
numIdxBits = self._getNumIdxBits(prefixOffset)
# Discard processed synaptic configuration
cIdsUnprocessed = cIdsUnprocessed[notMask]
wgtsUnprocessed = wgtsUnprocessed[notMask]
# Generate synapses and synEntries
synFmt = SynFmt(len(self._synFmts), cIdxOffset, cIdxMult,
numIdxBits, 0, self.numWeightBits,
Compression.DENSE, signMode, self.numDelayBits,
softReset)
self._synFmts.append(synFmt)
synEntry = SynEntry(prefixOffset, idx, wgts, synFmt, kIds, dlys)
self._synEntries.append(synEntry)
def _encodeSynDense1(self,
synIds,
weights,
cIdxOffset,
cIdxMult,
signMode,
kernelIds=None,
delays=None,
softReset=False):
"""Encode synapses using DENSE compression.
This encoder creates a new synaptic entry whenever a compartment
index increment greater than 1 is detected. That means no dummy
connections with zero weight are created.
:param np.ndarray synIds: Synapse indices.
:param np.ndarray kernelIds: Weight indices.
:param int cIdxOffset: cIdxOffset.
:param int cIdxMult: cIdxMult.
:param int signMode: Defines how inhibitory and excitatory weights are
treated. 1: Signs are not shared. 2: Excitatory connections with
shared sign. 3: Inhibitory connections with shared sign.
:param np.ndarray kernelIds: Global indices of synapse weights. Only
needed for plotting and reconstructing kernelIdMap to validate
partition.
:param np.ndarray delays: Delay values
:param bool softReset: True if synapse is used in soft-reset mode.
:return: List of synaptic entries.
:rtype: list[SynEntry]
"""
kIds = None
dlys = None
numSyn = len(synIds)
cIdStart = 0
# Find indices of gaps in the connection vector, in descending order.
gapIds = list(np.flatnonzero(np.diff(synIds) > 1))[::-1]
while cIdStart < numSyn:
# Compute id range of up to 60 synapses of current synEntry or
# until next non-1 compartment index increment
cIdEnd = min(cIdStart + self._maxNumSynPerSynEntry, numSyn)
if len(gapIds) and gapIds[-1] < cIdEnd:
cIdEnd = gapIds.pop() + 1
# Get synaptic configuration
idx = synIds[cIdStart:cIdEnd]
prefixOffset = idx[0]
if kernelIds is not None:
kIds = kernelIds[cIdStart:cIdEnd]
wgts = weights[cIdStart:cIdEnd]
if delays is not None:
dlys = delays[cIdStart:cIdEnd]
numIdxBits = self._getNumIdxBits(prefixOffset)
# Generate synapses and synEntries
synFmt = SynFmt(len(self._synFmts), cIdxOffset, cIdxMult,
numIdxBits, 0, self.numWeightBits,
Compression.DENSE, signMode, self.numDelayBits,
softReset)
self._synFmts.append(synFmt)
synEntry = SynEntry(prefixOffset, idx - prefixOffset, wgts, synFmt,
kIds, dlys)
self._synEntries.append(synEntry)
cIdStart = cIdEnd
def _encodeSynRunLength(self,
synIds,
weights,
cIdxOffset,
cIdxMult,
signMode,
kernelIds=None,
delays=None,
softReset=False):
"""Encode synapses using RUNLENGTH compression.
Creates a new ``SynEntry`` whenever it detects a gap >= 2**5 (limit on
``skipIdxBits``).
.. note:: Assumes that first skip index is zero!
:param np.ndarray synIds: Synapse indices.
:param np.ndarray kernelIds: Weight indices.
:param int cIdxOffset: cIdxOffset.
:param int cIdxMult: cIdxMult.
:param int signMode: Defines how inhibitory and excitatory weights are
treated. 1: Signs are not shared. 2: Excitatory connections with
shared sign. 3: Inhibitory connections with shared sign.
:param np.ndarray kernelIds: Global indices of synapse weights. Only
needed for plotting and reconstructing kernelIdMap to validate
partition.
:param np.ndarray delays: Delay values
:param bool softReset: True if synapse is used in soft-reset mode.
:return: List of synaptic entries.
:rtype: list[SynEntry]
"""
kIds = None
dlys = None
numSyn = len(synIds)
cIdStart = 0
# Find indices of gaps in the connection vector, in descending order.
gapIds = list(
np.flatnonzero(np.diff(synIds) >= 2**self._maxNumSkipBits))[::-1]
while cIdStart < numSyn:
# Compute id range of up to 60 synapses of current synEntry or
# until next compartment index increment >= 2**5.
cIdEnd = min(cIdStart + self._maxNumSynPerSynEntry, numSyn)
if len(gapIds) and gapIds[-1] < cIdEnd:
cIdEnd = gapIds.pop() + 1
# Get synaptic configuration
idx = synIds[cIdStart:cIdEnd]
skipIdx = np.insert(np.diff(idx), 0, 0)
prefixOffset = idx[0]
if kernelIds is not None:
kIds = kernelIds[cIdStart:cIdEnd]
wgts = weights[cIdStart:cIdEnd]
if delays is not None:
dlys = delays[cIdStart:cIdEnd]
numIdxBits = self._getNumIdxBits(prefixOffset)
numSkipBits = self._getNumSkipBits(np.max(skipIdx))
# Generate synapses and synEntries
synFmt = SynFmt(len(self._synFmts), cIdxOffset, cIdxMult,
numIdxBits, numSkipBits, self.numWeightBits,
Compression.RUNLENGTH, signMode, self.numDelayBits,
softReset)
self._synFmts.append(synFmt)
synEntry = SynEntry(prefixOffset, skipIdx, wgts, synFmt, kIds,
dlys)
self._synEntries.append(synEntry)
cIdStart = cIdEnd
def test_mapSynapses_3(self):
"""Check generation of synGrpMap."""
verbose = False
layer = Layer(layerId=0,
layerType='',
compartmentKwargs={},
connectionKwargs={"weightExponent": 2},
coreIdMap=np.array([]),
multiplicityMap=np.array([]),
postLayer=None)
sf = SynFmt(synFmtId=0,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=8,
compression=0,
signMode=1)
se0 = SynEntry(prefixOffset=0,
idxs=np.array([0, 1, 2]),
weights=np.array([6, 7, 8]),
synFmt=sf)
se1 = SynEntry(prefixOffset=10,
idxs=np.array([0, 1]),
weights=np.array([16, 17]),
synFmt=sf)
se2 = SynEntry(prefixOffset=20,
idxs=np.array([0, 1, 2]),
weights=np.array([26, 27, 28]),
synFmt=sf)
se3 = SynEntry(prefixOffset=30,
idxs=np.array([0, 1]),
weights=np.array([36, 37]),
synFmt=sf)
sg0 = SynapseGroup(groupId=0, synEntries=[[se0, se1, se2], [se3]])
sg1 = SynapseGroup(groupId=1, synEntries=[[se0], [se1], [se2, se3]])
p = Partition(partitionId=0,
chipCounter=0,
sizeInterleaved=-1,
parentLayer=layer)
p.addSynFmt(sf)
p.addSynapseGroup(sg0)
p.addSynapseGroup(sg1)
layer.addPartition(p)
numSyn = 10 * 2
board = N2Board(0)
c = DnnMapper(board)
core = board.allocateCores(1, numSyn)[0]
c._mapSynapses(p, core) # pylint: disable=protected-access
if verbose:
print(c._synGrpMap)
self.assertEqual(len(c._synGrpMap), 5)
self.assertEqual(c._synGrpMap[(sg0, 0)], (0, 8))
self.assertEqual(c._synGrpMap[(sg0, 1)], (8, 2))
self.assertEqual(c._synGrpMap[(sg1, 0)], (10, 3))
self.assertEqual(c._synGrpMap[(sg1, 1)], (13, 2))
self.assertEqual(c._synGrpMap[(sg1, 2)], (15, 5))
def test_mapSynapses_2(self):
"""Check switching between synFmt for successive synEntries if
synFmtId does not change."""
verbose = False
layer = Layer(layerId=0,
layerType='',
compartmentKwargs={},
connectionKwargs={"weightExponent": 2},
coreIdMap=np.array([]),
multiplicityMap=np.array([]),
postLayer=None)
sf0 = SynFmt(synFmtId=0,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=8,
compression=0,
signMode=1)
sf1 = SynFmt(synFmtId=1,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=7,
numSkipBits=0,
numWgtBits=8,
compression=1,
signMode=1)
se00 = SynEntry(prefixOffset=0,
idxs=np.array([0, 1, 2]),
weights=np.array([6, 7, 8]),
synFmt=sf0)
se01 = SynEntry(prefixOffset=10,
idxs=np.array([0, 1]),
weights=np.array([16, 17]),
synFmt=sf0)
se02 = SynEntry(prefixOffset=20,
idxs=np.array([0, 1, 2]),
weights=np.array([26, 27, 28]),
synFmt=sf0)
se10 = SynEntry(prefixOffset=30,
idxs=np.array([0, 1]),
weights=np.array([36, 37]),
synFmt=sf1)
sg = SynapseGroup(groupId=0, synEntries=[[se00, se01, se02], [se10]])
p = Partition(partitionId=0,
chipCounter=0,
sizeInterleaved=-1,
parentLayer=layer)
p.addSynFmt(sf0)
p.addSynFmt(sf1)
p.addSynapseGroup(sg)
layer.addPartition(p)
numSyn = 10
board = N2Board(0)
c = DnnMapper(board)
core = board.allocateCores(1, numSyn)[0]
c._mapSynapses(p, core) # pylint: disable=protected-access
if verbose:
c.printCore(core, synapses=True)
cIdx = [0, 1, 2, 10, 11, 20, 21, 22, 30, 31]
wgts = [6, 7, 8, 16, 17, 26, 27, 28, 36, 37]
synFmtIds = [1, 1, 1, 2, 2, 1, 1, 1, 3, 3]
for i in range(numSyn):
self.assertEqual(core.synapses[i].CIdx, cIdx[i])
self.assertEqual(core.synapses[i].Wgt, wgts[i])
self.assertEqual(core.synapses[i].synFmtId, synFmtIds[i])
def test_mapSynapses_1(self):
"""Check allocation of synFmts."""
verbose = False
layer = Layer(layerId=0,
layerType='',
compartmentKwargs={},
connectionKwargs={"weightExponent": 2},
coreIdMap=np.array([]),
multiplicityMap=np.array([]),
postLayer=None)
sf0 = SynFmt(synFmtId=0,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=8,
compression=0,
signMode=2)
sf1 = SynFmt(synFmtId=1,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=6,
compression=0,
signMode=3)
p = Partition(partitionId=0,
chipCounter=0,
sizeInterleaved=-1,
parentLayer=layer)
p.addSynFmt(sf0)
p.addSynFmt(sf1)
layer.addPartition(p)
board = N2Board(0)
c = DnnMapper(board)
core = board.allocateCores(1, 0)[0]
c._mapSynapses(p, core) # pylint: disable=protected-access
if verbose:
c.printCore(core, synFmts=True)
# Validate number of synFmts
self.assertEqual(len(core.synapseFmt.modified), 4)
# Validate duplication of synFmts by checking unique fanoutTypes
self.assertEqual(core.synapseFmt[1].fanoutType, 2)
self.assertEqual(core.synapseFmt[2].fanoutType, 2)
self.assertEqual(core.synapseFmt[3].fanoutType, 3)
self.assertEqual(core.synapseFmt[4].fanoutType, 3)
# Validate mapping of numWgtBits -> wgtBits
self.assertEqual(core.synapseFmt[1].wgtBits, 7)
self.assertEqual(core.synapseFmt[3].wgtBits, 6)
# Validate wgtExp
self.assertEqual(core.synapseFmt[1].wgtExp, 2)
def test_mapInputAxons_1(self):
"""Check mapping of InputAxonGroup with all input nodes having
multiplicity 1."""
verbose = False
layer = Layer(layerId=0,
layerType='',
compartmentKwargs={},
connectionKwargs={"weightExponent": 2},
coreIdMap=np.array([]),
multiplicityMap=np.array([]),
postLayer=None)
sf = SynFmt(synFmtId=0,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=8,
compression=0,
signMode=1)
se0 = SynEntry(prefixOffset=0,
idxs=np.array([0, 1, 2]),
weights=np.array([6, 7, 8]),
synFmt=sf)
se1 = SynEntry(prefixOffset=10,
idxs=np.array([0, 1]),
weights=np.array([16, 17]),
synFmt=sf)
se2 = SynEntry(prefixOffset=20,
idxs=np.array([0, 1, 2]),
weights=np.array([26, 27, 28]),
synFmt=sf)
se3 = SynEntry(prefixOffset=30,
idxs=np.array([0, 1]),
weights=np.array([36, 37]),
synFmt=sf)
sg = SynapseGroup(groupId=0,
synEntries=[[se0, se1], [se1, se2, se3], [se3]])
p = Partition(partitionId=0,
chipCounter=0,
sizeInterleaved=-1,
parentLayer=layer)
p.addSynFmt(sf)
p.addSynapseGroup(sg)
iag = InputAxonGroup(srcNodeIds=np.array([10, 11, 12]),
multiplicity=np.array([1, 1, 1]),
synGroup=sg,
cxBase=2,
parentPartition=p)
p.addInputAxonGroup(iag)
layer.addPartition(p)
numSyn = 14
board = N2Board(0)
c = DnnMapper(board)
core = board.allocateCores(1, numSyn)[0]
c._mapSynapses(p, core) # pylint: disable=protected-access
c._mapInputAxons(p, core) # pylint: disable=protected-access
# Validate input axon
sMap = core.synapseMap[0]
self.assertEqual(sMap.synapsePtr, 0)
self.assertEqual(sMap.synapseLen, [5, 7, 2])
self.assertEqual(sMap.popSize, 3)
self.assertEqual(sMap.population32MapEntry.ptr, 0)
self.assertEqual(sMap.population32MapEntry.length, 3)
self.assertEqual(sMap.population32MapEntry.cxBase, 2)
# Validate inAxMap
self.assertTrue(
np.array_equal(c._inAxMap[iag.id],
np.array([[0, 0, 4], [0, 0, 4], [0, 0, 4]], int)))
if verbose:
c.printCore(core, inputAxons=True)
def test_mapInputAxons_4(self):
"""Check mapping of multiple InputAxonGroups."""
verbose = False
layer = Layer(layerId=0,
layerType='',
compartmentKwargs={},
connectionKwargs={"weightExponent": 2},
coreIdMap=np.array([]),
multiplicityMap=np.array([]),
postLayer=None)
sf = SynFmt(synFmtId=0,
cIdxOffset=0,
cIdxMult=2,
numIdxBits=6,
numSkipBits=0,
numWgtBits=8,
compression=0,
signMode=1)
se0 = SynEntry(prefixOffset=0,
idxs=np.array([0, 1, 2]),
weights=np.array([6, 7, 8]),
synFmt=sf)
se1 = SynEntry(prefixOffset=10,
idxs=np.array([0, 1]),
weights=np.array([16, 17]),
synFmt=sf)
se2 = SynEntry(prefixOffset=20,
idxs=np.array([0, 1, 2]),
weights=np.array([26, 27, 28]),
synFmt=sf)
se3 = SynEntry(prefixOffset=30,
idxs=np.array([0, 1]),
weights=np.array([36, 37]),
synFmt=sf)
sg0 = SynapseGroup(groupId=0,
synEntries=[[se0, se1], [se1, se2, se3], [se3]])
sg1 = SynapseGroup(groupId=1,
synEntries=[[se0, se1], [se2], [se1, se2, se3]])
p = Partition(partitionId=0,
chipCounter=0,
sizeInterleaved=-1,
parentLayer=layer)
p.addSynFmt(sf)
p.addSynapseGroup(sg0)
p.addSynapseGroup(sg1)
iag0 = InputAxonGroup(srcNodeIds=np.array([10, 11, 12]),
multiplicity=np.array([1, 1, 1]),
synGroup=sg0,
cxBase=1,
parentPartition=p)
iag1 = InputAxonGroup(srcNodeIds=np.array([10, 11, 12]),
multiplicity=np.array([1, 2, 1]),
synGroup=sg0,
cxBase=2,
parentPartition=p)
iag2 = InputAxonGroup(srcNodeIds=np.array([10, 11, 12]),
multiplicity=np.array([1, 2, 3]),
synGroup=sg1,
cxBase=3,
parentPartition=p)
p.addInputAxonGroup(iag0)
p.addInputAxonGroup(iag1)
p.addInputAxonGroup(iag2)
layer.addPartition(p)
board = N2Board(0)
c = DnnMapper(board)
core = board.allocateCores(1, p.numSyn)[0]
c._mapSynapses(p, core) # pylint: disable=protected-access
c._mapInputAxons(p, core) # pylint: disable=protected-access
# Validate axons of iag0
sMapShared = core.synapseMap[0]
self.assertEqual(sMapShared.synapsePtr, 0)
self.assertEqual(sMapShared.synapseLen, [5, 7, 2])
self.assertEqual(sMapShared.popSize, 3)
self.assertEqual(sMapShared.population32MapEntry.ptr, 0)
self.assertEqual(sMapShared.population32MapEntry.length, 3)
self.assertEqual(sMapShared.population32MapEntry.cxBase, 1)
# Validate axons of iag1
sMapShared = core.synapseMap[1]
self.assertEqual(sMapShared.synapsePtr, 0)
self.assertEqual(sMapShared.synapseLen, [5, 7, 2])
self.assertEqual(sMapShared.popSize, 3)
self.assertEqual(sMapShared.population32MapEntry.ptr, 0)
self.assertEqual(sMapShared.population32MapEntry.length, 3)
self.assertEqual(sMapShared.population32MapEntry.cxBase, 2)
sMapDiscrete = core.synapseMap[2]
self.assertEqual(sMapDiscrete.synapsePtr, 5)
self.assertEqual(sMapDiscrete.synapseLen, 7)
self.assertEqual(sMapDiscrete.discreteMapEntry.ptr, 3)
self.assertEqual(sMapDiscrete.discreteMapEntry.length, 3)
self.assertEqual(sMapDiscrete.discreteMapEntry.cxBase, 2)
# Validate axons of iag2
sMapShared = core.synapseMap[3]
self.assertEqual(sMapShared.synapsePtr, 14)
self.assertEqual(sMapShared.synapseLen, [5, 3, 7])
self.assertEqual(sMapShared.popSize, 3)
self.assertEqual(sMapShared.population32MapEntry.ptr, 9)
self.assertEqual(sMapShared.population32MapEntry.length, 3)
self.assertEqual(sMapShared.population32MapEntry.cxBase, 3)
sMapDiscrete = core.synapseMap[4]
self.assertEqual(sMapDiscrete.synapsePtr, 19)
self.assertEqual(sMapDiscrete.synapseLen, 3)
self.assertEqual(sMapDiscrete.discreteMapEntry.ptr, 12)
self.assertEqual(sMapDiscrete.discreteMapEntry.length, 1)
self.assertEqual(sMapDiscrete.discreteMapEntry.cxBase, 3)
sMapDiscrete = core.synapseMap[5]
self.assertEqual(sMapDiscrete.synapsePtr, 22)
self.assertEqual(sMapDiscrete.synapseLen, 7)
self.assertEqual(sMapDiscrete.discreteMapEntry.ptr, 15)
self.assertEqual(sMapDiscrete.discreteMapEntry.length, 3)
self.assertEqual(sMapDiscrete.discreteMapEntry.cxBase, 3)
# Validate inAxMap
self.assertTrue(
np.array_equal(c._inAxMap[iag0.id],
np.array([[0, 0, 4], [0, 0, 4], [0, 0, 4]], int)))
self.assertTrue(
np.array_equal(c._inAxMap[iag1.id],
np.array([[1, 0, 4], [2, 0, 4], [1, 0, 4]], int)))
self.assertTrue(
np.array_equal(c._inAxMap[iag2.id],
np.array([[3, 0, 4], [4, 0, 4], [5, 0, 4]], int)))
if verbose:
c.printCore(core, inputAxons=True)