def createCellsFixedNum(self):
''' Create population cells based on fixed number of cells'''
cellModelClass = f.Cell
cells = []
seed(f.sim.id32('%d'%(f.cfg['seeds']['loc']+self.tags['numCells']+f.net.lastGid)))
randLocs = rand(self.tags['numCells'], 3) # create random x,y,z locations
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord+'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord+'normRange'] = [float(point) / f.net.params['size'+coord.upper()] for point in self.tags[coord+'Range']]
if coord+'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
randLocs[:,icoord] = randLocs[:,icoord] * (maxv-minv) + minv
for i in xrange(int(f.rank), f.net.params['scale'] * self.tags['numCells'], f.nhosts):
gid = f.net.lastGid+i
self.cellGids.append(gid) # add gid list of cells belonging to this population - not needed?
cellTags = {k: v for (k, v) in self.tags.iteritems() if k in f.net.params['popTagsCopiedToCells']} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['xnorm'] = randLocs[i,0] # set x location (um)
cellTags['ynorm'] = randLocs[i,1] # set y location (um)
cellTags['znorm'] = randLocs[i,2] # set z location (um)
cellTags['x'] = f.net.params['sizeX'] * randLocs[i,0] # set x location (um)
cellTags['y'] = f.net.params['sizeY'] * randLocs[i,1] # set y location (um)
cellTags['z'] = f.net.params['sizeZ'] * randLocs[i,2] # set z location (um)
if 'propList' not in cellTags: cellTags['propList'] = [] # initalize list of property sets if doesn't exist
cells.append(cellModelClass(gid, cellTags)) # instantiate Cell object
if f.cfg['verbose']: print('Cell %d/%d (gid=%d) of pop %s, on node %d, '%(i, f.net.params['scale'] * self.tags['numCells']-1, gid, self.tags['popLabel'], f.rank))
f.net.lastGid = f.net.lastGid + self.tags['numCells']
return cells
def createCellsFixedNum (self):
''' Create population cells based on fixed number of cells'''
cells = []
seed(sim.id32('%d'%(sim.cfg.seeds['loc']+self.tags['numCells']+sim.net.lastGid)))
randLocs = rand(self.tags['numCells'], 3) # create random x,y,z locations
if sim.net.params.shape == 'cylinder':
# Use the x,z random vales
rho = randLocs[:,0] # use x rand value as the radius rho in the interval [0, 1)
phi = 2 * pi * randLocs[:,2] # use z rand value as the angle phi in the interval [0, 2*pi)
x = (1 + sqrt(rho) * cos(phi))/2.0
z = (1 + sqrt(rho) * sin(phi))/2.0
randLocs[:,0] = x
randLocs[:,2] = z
elif sim.net.params.shape == 'ellipsoid':
# Use the x,y,z random vales
rho = np.power(randLocs[:,0], 1.0/3.0) # use x rand value as the radius rho in the interval [0, 1); cuberoot
phi = 2 * pi * randLocs[:,1] # use y rand value as the angle phi in the interval [0, 2*pi)
costheta = (2 * randLocs[:,2]) - 1 # use z rand value as cos(theta) in the interval [-1, 1); ensures uniform dist
theta = arccos(costheta) # obtain theta from cos(theta)
x = (1 + rho * cos(phi) * sin(theta))/2.0
y = (1 + rho * sin(phi) * sin(theta))/2.0
z = (1 + rho * cos(theta))/2.0
randLocs[:,0] = x
randLocs[:,1] = y
randLocs[:,2] = z
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord+'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord+'normRange'] = [float(point) / getattr(sim.net.params, 'size'+coord.upper()) for point in self.tags[coord+'Range']]
# constrain to range set by user
if coord+'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
randLocs[:,icoord] = randLocs[:,icoord] * (maxv-minv) + minv
for i in self._distributeCells(int(sim.net.params.scale * self.tags['numCells']))[sim.rank]:
gid = sim.net.lastGid+i
self.cellGids.append(gid) # add gid list of cells belonging to this population - not needed?
cellTags = {k: v for (k, v) in self.tags.iteritems() if k in sim.net.params.popTagsCopiedToCells} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['popLabel'] = self.tags['popLabel']
cellTags['xnorm'] = randLocs[i,0] # set x location (um)
cellTags['ynorm'] = randLocs[i,1] # set y location (um)
cellTags['znorm'] = randLocs[i,2] # set z location (um)
cellTags['x'] = sim.net.params.sizeX * randLocs[i,0] # set x location (um)
cellTags['y'] = sim.net.params.sizeY * randLocs[i,1] # set y location (um)
cellTags['z'] = sim.net.params.sizeZ * randLocs[i,2] # set z location (um)
cells.append(self.cellModelClass(gid, cellTags)) # instantiate Cell object
if sim.cfg.verbose: print('Cell %d/%d (gid=%d) of pop %s, on node %d, '%(i, sim.net.params.scale * self.tags['numCells']-1, gid, self.tags['popLabel'], sim.rank))
sim.net.lastGid = sim.net.lastGid + self.tags['numCells']
return cells
def createCellsFixedNum(self):
''' Create population cells based on fixed number of cells'''
cellModelClass = sim.Cell
cells = []
seed(
sim.id32('%d' % (sim.cfg.seeds['loc'] + self.tags['numCells'] +
sim.net.lastGid)))
randLocs = rand(self.tags['numCells'],
3) # create random x,y,z locations
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord + 'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord + 'normRange'] = [
float(point) /
getattr(sim.net.params, 'size' + coord.upper())
for point in self.tags[coord + 'Range']
]
if coord + 'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord + 'normRange'][0]
maxv = self.tags[coord + 'normRange'][1]
randLocs[:,
icoord] = randLocs[:, icoord] * (maxv - minv) + minv
for i in xrange(int(sim.rank),
sim.net.params.scale * self.tags['numCells'],
sim.nhosts):
gid = sim.net.lastGid + i
self.cellGids.append(
gid
) # add gid list of cells belonging to this population - not needed?
cellTags = {
k: v
for (k, v) in self.tags.iteritems()
if k in sim.net.params.popTagsCopiedToCells
} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['popLabel'] = self.tags['popLabel']
cellTags['xnorm'] = randLocs[i, 0] # set x location (um)
cellTags['ynorm'] = randLocs[i, 1] # set y location (um)
cellTags['znorm'] = randLocs[i, 2] # set z location (um)
cellTags['x'] = sim.net.params.sizeX * randLocs[
i, 0] # set x location (um)
cellTags['y'] = sim.net.params.sizeY * randLocs[
i, 1] # set y location (um)
cellTags['z'] = sim.net.params.sizeZ * randLocs[
i, 2] # set z location (um)
cells.append(cellModelClass(gid,
cellTags)) # instantiate Cell object
if sim.cfg.verbose:
print('Cell %d/%d (gid=%d) of pop %s, on node %d, ' %
(i, sim.net.params.scale * self.tags['numCells'] - 1,
gid, self.tags['popLabel'], sim.rank))
sim.net.lastGid = sim.net.lastGid + self.tags['numCells']
return cells
def createCellsGrid (self):
''' Create population cells based on fixed number of cells'''
cells = []
seed(sim.id32('%d'%(sim.cfg.seeds['loc']+self.tags['gridSpacing']+sim.net.lastGid)))
rangeLocs = [[0, getattr(sim.net.params, 'size'+coord)] for coord in ['X','Y','Z']]
for icoord, coord in enumerate(['x', 'y', 'z']):
# constrain to range set by user
if coord+'normRange' in self.tags: # if normalized range, convert to normalized
self.tags[coord+'Range'] = [float(point) * getattr(sim.net.params, 'size'+coord.upper()) for point in self.tags[coord+'Range']]
if coord+'Range' in self.tags: # if user provided absolute range, calculate range
self.tags[coord+'normRange'] = [float(point) / getattr(sim.net.params, 'size'+coord.upper()) for point in self.tags[coord+'Range']]
rangeLocs[icoord] = [self.tags[coord+'Range'][0], self.tags[coord+'Range'][1]]
gridSpacing = self.tags['gridSpacing']
gridLocs = []
for x in np.arange(rangeLocs[0][0], rangeLocs[0][1]+1, gridSpacing):
for y in np.arange(rangeLocs[1][0], rangeLocs[1][1]+1, gridSpacing):
for z in np.arange(rangeLocs[2][0], rangeLocs[2][1]+1, gridSpacing):
gridLocs.append((x, y, z))
numCells = len(gridLocs)
for i in self._distributeCells(numCells)[sim.rank]:
gid = sim.net.lastGid+i
self.cellGids.append(gid) # add gid list of cells belonging to this population - not needed?
cellTags = {k: v for (k, v) in self.tags.iteritems() if k in sim.net.params.popTagsCopiedToCells} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['popLabel'] = self.tags['popLabel']
cellTags['xnorm'] = gridLocs[i][0] / sim.net.params.sizeX # set x location (um)
cellTags['ynorm'] = gridLocs[i][1] / sim.net.params.sizeY # set y location (um)
cellTags['znorm'] = gridLocs[i][2] / sim.net.params.sizeZ # set z location (um)
cellTags['x'] = gridLocs[i][0] # set x location (um)
cellTags['y'] = gridLocs[i][1] # set y location (um)
cellTags['z'] = gridLocs[i][2] # set z location (um)
cells.append(self.cellModelClass(gid, cellTags)) # instantiate Cell object
if sim.cfg.verbose: print('Cell %d/%d (gid=%d) of pop %s, on node %d, '%(i, numCells, gid, self.tags['popLabel'], sim.rank))
sim.net.lastGid = sim.net.lastGid + numCells
return cells
def createCellsDensity (self):
''' Create population cells based on density'''
cells = []
shape = sim.net.params.shape
sizeX = sim.net.params.sizeX
sizeY = sim.net.params.sizeY
sizeZ = sim.net.params.sizeZ
# calculate volume
if shape == 'cuboid':
volume = sizeY/1e3 * sizeX/1e3 * sizeZ/1e3
elif shape == 'cylinder':
volume = sizeY/1e3 * sizeX/1e3/2 * sizeZ/1e3/2 * pi
elif shape == 'ellipsoid':
volume = sizeY/1e3/2.0 * sizeX/1e3/2.0 * sizeZ/1e3/2.0 * pi * 4.0 / 3.0
for coord in ['x', 'y', 'z']:
if coord+'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord+'normRange'] = [point / sim.net.params['size'+coord.upper()] for point in self.tags[coord+'Range']]
if coord+'normRange' in self.tags: # if normalized range, rescale volume
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
volume = volume * (maxv-minv)
funcLocs = None # start with no locations as a function of density function
if isinstance(self.tags['density'], str): # check if density is given as a function
if shape == 'cuboid': # only available for cuboids
strFunc = self.tags['density'] # string containing function
strVars = [var for var in ['xnorm', 'ynorm', 'znorm'] if var in strFunc] # get list of variables used
if not len(strVars) == 1:
print 'Error: density function (%s) for population %s does not include "xnorm", "ynorm" or "znorm"'%(strFunc,self.tags['popLabel'])
return
coordFunc = strVars[0]
lambdaStr = 'lambda ' + coordFunc +': ' + strFunc # convert to lambda function
densityFunc = eval(lambdaStr)
minRange = self.tags[coordFunc+'Range'][0]
maxRange = self.tags[coordFunc+'Range'][1]
interval = 0.001 # interval of location values to evaluate func in order to find the max cell density
maxDensity = max(map(densityFunc, (arange(minRange, maxRange, interval)))) # max cell density
maxCells = volume * maxDensity # max number of cells based on max value of density func
seed(sim.id32('%d' % sim.cfg.seeds['loc']+sim.net.lastGid)) # reset random number generator
locsAll = minRange + ((maxRange-minRange)) * rand(int(maxCells), 1) # random location values
locsProb = array(map(densityFunc, locsAll)) / maxDensity # calculate normalized density for each location value (used to prune)
allrands = rand(len(locsProb)) # create an array of random numbers for checking each location pos
makethiscell = locsProb>allrands # perform test to see whether or not this cell should be included (pruning based on density func)
funcLocs = [locsAll[i] for i in range(len(locsAll)) if i in array(makethiscell.nonzero()[0],dtype='int')] # keep only subset of yfuncLocs based on density func
self.tags['numCells'] = len(funcLocs) # final number of cells after pruning of location values based on density func
if sim.cfg.verbose: print 'Volume=%.2f, maxDensity=%.2f, maxCells=%.0f, numCells=%.0f'%(volume, maxDensity, maxCells, self.tags['numCells'])
else:
print 'Error: Density functions are only implemented for cuboid shaped networks'
exit(0)
else: # NO ynorm-dep
self.tags['numCells'] = int(self.tags['density'] * volume) # = density (cells/mm^3) * volume (mm^3)
# calculate locations of cells
seed(sim.id32('%d'%(sim.cfg.seeds['loc']+self.tags['numCells']+sim.net.lastGid)))
randLocs = rand(self.tags['numCells'], 3) # create random x,y,z locations
if sim.net.params.shape == 'cylinder':
# Use the x,z random vales
rho = randLocs[:,0] # use x rand value as the radius rho in the interval [0, 1)
phi = 2 * pi * randLocs[:,2] # use z rand value as the angle phi in the interval [0, 2*pi)
x = (1 + sqrt(rho) * cos(phi))/2.0
z = (1 + sqrt(rho) * sin(phi))/2.0
randLocs[:,0] = x
randLocs[:,2] = z
elif sim.net.params.shape == 'ellipsoid':
# Use the x,y,z random vales
rho = np.power(randLocs[:,0], 1.0/3.0) # use x rand value as the radius rho in the interval [0, 1); cuberoot
phi = 2 * pi * randLocs[:,1] # use y rand value as the angle phi in the interval [0, 2*pi)
costheta = (2 * randLocs[:,2]) - 1 # use z rand value as cos(theta) in the interval [-1, 1); ensures uniform dist
theta = arccos(costheta) # obtain theta from cos(theta)
x = (1 + rho * cos(phi) * sin(theta))/2.0
y = (1 + rho * sin(phi) * sin(theta))/2.0
z = (1 + rho * cos(theta))/2.0
randLocs[:,0] = x
randLocs[:,1] = y
randLocs[:,2] = z
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord+'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
randLocs[:,icoord] = randLocs[:,icoord] * (maxv-minv) + minv
if funcLocs and coordFunc == coord+'norm': # if locations for this coordinate calculated using density function
randLocs[:,icoord] = funcLocs
if sim.cfg.verbose and not funcLocs: print 'Volume=%.4f, density=%.2f, numCells=%.0f'%(volume, self.tags['density'], self.tags['numCells'])
for i in self._distributeCells(self.tags['numCells'])[sim.rank]:
gid = sim.net.lastGid+i
self.cellGids.append(gid) # add gid list of cells belonging to this population - not needed?
cellTags = {k: v for (k, v) in self.tags.iteritems() if k in sim.net.params.popTagsCopiedToCells} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['popLabel'] = self.tags['popLabel']
cellTags['xnorm'] = randLocs[i,0] # calculate x location (um)
cellTags['ynorm'] = randLocs[i,1] # calculate y location (um)
cellTags['znorm'] = randLocs[i,2] # calculate z location (um)
cellTags['x'] = sizeX * randLocs[i,0] # calculate x location (um)
cellTags['y'] = sizeY * randLocs[i,1] # calculate y location (um)
cellTags['z'] = sizeZ * randLocs[i,2] # calculate z location (um)
cells.append(self.cellModelClass(gid, cellTags)) # instantiate Cell object
if sim.cfg.verbose:
print('Cell %d/%d (gid=%d) of pop %s, pos=(%2.f, %2.f, %2.f), on node %d, '%(i, self.tags['numCells']-1, gid, self.tags['popLabel'],cellTags['x'], cellTags['y'], cellTags['z'], sim.rank))
sim.net.lastGid = sim.net.lastGid + self.tags['numCells']
return cells
def createCellsDensity(self):
''' Create population cells based on density'''
cellModelClass = sim.Cell
cells = []
volume = sim.net.params.sizeY / 1e3 * sim.net.params.sizeX / 1e3 * sim.net.params.sizeZ / 1e3 # calculate full volume
for coord in ['x', 'y', 'z']:
if coord + 'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord + 'normRange'] = [
point / sim.net.params['size' + coord.upper()]
for point in self.tags[coord + 'Range']
]
if coord + 'normRange' in self.tags: # if normalized range, rescale volume
minv = self.tags[coord + 'normRange'][0]
maxv = self.tags[coord + 'normRange'][1]
volume = volume * (maxv - minv)
funcLocs = None # start with no locations as a function of density function
if isinstance(self.tags['density'],
str): # check if density is given as a function
strFunc = self.tags['density'] # string containing function
strVars = [
var for var in ['xnorm', 'ynorm', 'znorm'] if var in strFunc
] # get list of variables used
if not len(strVars) == 1:
print 'Error: density function (%s) for population %s does not include "xnorm", "ynorm" or "znorm"' % (
strFunc, self.tags['popLabel'])
return
coordFunc = strVars[0]
lambdaStr = 'lambda ' + coordFunc + ': ' + strFunc # convert to lambda function
densityFunc = eval(lambdaStr)
minRange = self.tags[coordFunc + 'Range'][0]
maxRange = self.tags[coordFunc + 'Range'][1]
interval = 0.001 # interval of location values to evaluate func in order to find the max cell density
maxDensity = max(
map(densityFunc, (arange(minRange, maxRange,
interval)))) # max cell density
maxCells = volume * maxDensity # max number of cells based on max value of density func
seed(sim.id32('%d' % sim.cfg.seeds['loc'] +
sim.net.lastGid)) # reset random number generator
locsAll = minRange + ((maxRange - minRange)) * rand(
int(maxCells), 1) # random location values
locsProb = array(
map(densityFunc, locsAll)
) / maxDensity # calculate normalized density for each location value (used to prune)
allrands = rand(
len(locsProb)
) # create an array of random numbers for checking each location pos
makethiscell = locsProb > allrands # perform test to see whether or not this cell should be included (pruning based on density func)
funcLocs = [
locsAll[i] for i in range(len(locsAll))
if i in array(makethiscell.nonzero()[0], dtype='int')
] # keep only subset of yfuncLocs based on density func
self.tags['numCells'] = len(
funcLocs
) # final number of cells after pruning of location values based on density func
if sim.cfg.verbose:
print 'Volume=%.2f, maxDensity=%.2f, maxCells=%.0f, numCells=%.0f' % (
volume, maxDensity, maxCells, self.tags['numCells'])
else: # NO ynorm-dep
self.tags['numCells'] = int(
self.tags['density'] *
volume) # = density (cells/mm^3) * volume (mm^3)
# calculate locations of cells
seed(
sim.id32('%d' % (sim.cfg.seeds['loc'] + self.tags['numCells'] +
sim.net.lastGid)))
randLocs = rand(self.tags['numCells'],
3) # create random x,y,z locations
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord + 'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord + 'normRange'][0]
maxv = self.tags[coord + 'normRange'][1]
randLocs[:,
icoord] = randLocs[:, icoord] * (maxv - minv) + minv
if funcLocs and coordFunc == coord + 'norm': # if locations for this coordinate calcualated using density function
randLocs[:, icoord] = funcLocs
if sim.cfg.verbose and not funcLocs:
print 'Volume=%.4f, density=%.2f, numCells=%.0f' % (
volume, self.tags['density'], self.tags['numCells'])
for i in xrange(int(sim.rank), self.tags['numCells'], sim.nhosts):
gid = sim.net.lastGid + i
self.cellGids.append(
gid
) # add gid list of cells belonging to this population - not needed?
cellTags = {
k: v
for (k, v) in self.tags.iteritems()
if k in sim.net.params.popTagsCopiedToCells
} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['popLabel'] = self.tags['popLabel']
cellTags['xnorm'] = randLocs[i, 0] # calculate x location (um)
cellTags['ynorm'] = randLocs[i, 1] # calculate y location (um)
cellTags['znorm'] = randLocs[i, 2] # calculate z location (um)
cellTags['x'] = sim.net.params.sizeX * randLocs[
i, 0] # calculate x location (um)
cellTags['y'] = sim.net.params.sizeY * randLocs[
i, 1] # calculate y location (um)
cellTags['z'] = sim.net.params.sizeZ * randLocs[
i, 2] # calculate z location (um)
cells.append(cellModelClass(gid,
cellTags)) # instantiate Cell object
if sim.cfg.verbose:
print(
'Cell %d/%d (gid=%d) of pop %s, pos=(%2.f, %2.f, %2.f), on node %d, '
%
(i, self.tags['numCells'] - 1, gid, self.tags['popLabel'],
cellTags['x'], cellTags['y'], cellTags['z'], sim.rank))
sim.net.lastGid = sim.net.lastGid + self.tags['numCells']
return cells
def createCellsDensity(self):
''' Create population cells based on density'''
cellModelClass = f.Cell
cells = []
volume = f.net.params['sizeY']/1e3 * f.net.params['sizeX']/1e3 * f.net.params['sizeZ']/1e3 # calculate full volume
for coord in ['x', 'y', 'z']:
if coord+'Range' in self.tags: # if user provided absolute range, convert to normalized
self.tags[coord+'normRange'] = [point / f.net.params['size'+coord.upper()] for point in self.tags[coord+'Range']]
if coord+'normRange' in self.tags: # if normalized range, rescale volume
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
volume = volume * (maxv-minv)
funcLocs = None # start with no locations as a function of density function
if isinstance(self.tags['density'], str): # check if density is given as a function
strFunc = self.tags['density'] # string containing function
strVars = [var for var in ['xnorm', 'ynorm', 'znorm'] if var in strFunc] # get list of variables used
if not len(strVars) == 1:
print 'Error: density function (%s) for population %s does not include "xnorm", "ynorm" or "znorm"'%(strFunc,self.tags['popLabel'])
return
coordFunc = strVars[0]
lambdaStr = 'lambda ' + coordFunc +': ' + strFunc # convert to lambda function
densityFunc = eval(lambdaStr)
minRange = self.tags[coordFunc+'Range'][0]
maxRange = self.tags[coordFunc+'Range'][1]
interval = 0.001 # interval of location values to evaluate func in order to find the max cell density
maxDensity = max(map(densityFunc, (arange(minRange, maxRange, interval)))) # max cell density
maxCells = volume * maxDensity # max number of cells based on max value of density func
seed(f.sim.id32('%d' % f.cfg['seeds']['loc'])) # reset random number generator
locsAll = minRange + ((maxRange-minRange)) * rand(int(maxCells), 1) # random location values
locsProb = array(map(densityFunc, locsAll)) / maxDensity # calculate normalized density for each location value (used to prune)
allrands = rand(len(locsProb)) # create an array of random numbers for checking each location pos
makethiscell = locsProb>allrands # perform test to see whether or not this cell should be included (pruning based on density func)
funcLocs = [locsAll[i] for i in range(len(locsAll)) if i in array(makethiscell.nonzero()[0],dtype='int')] # keep only subset of yfuncLocs based on density func
self.tags['numCells'] = len(funcLocs) # final number of cells after pruning of location values based on density func
if f.cfg['verbose']: print 'Volume=%.2f, maxDensity=%.2f, maxCells=%.0f, numCells=%.0f'%(volume, maxDensity, maxCells, self.tags['numCells'])
else: # NO ynorm-dep
self.tags['numCells'] = int(self.tags['density'] * volume) # = density (cells/mm^3) * volume (mm^3)
# calculate locations of cells
seed(f.sim.id32('%d'%(f.cfg['seeds']['loc']+self.tags['numCells'])))
randLocs = rand(self.tags['numCells'], 3) # create random x,y,z locations
for icoord, coord in enumerate(['x', 'y', 'z']):
if coord+'normRange' in self.tags: # if normalized range, rescale random locations
minv = self.tags[coord+'normRange'][0]
maxv = self.tags[coord+'normRange'][1]
randLocs[:,icoord] = randLocs[:,icoord] * (maxv-minv) + minv
if funcLocs and coordFunc == coord+'norm': # if locations for this coordinate calcualated using density function
randLocs[:,icoord] = funcLocs
if f.cfg['verbose'] and not funcLocs: print 'Volume=%.4f, density=%.2f, numCells=%.0f'%(volume, self.tags['density'], self.tags['numCells'])
for i in xrange(int(f.rank), self.tags['numCells'], f.nhosts):
gid = f.net.lastGid+i
self.cellGids.append(gid) # add gid list of cells belonging to this population - not needed?
cellTags = {k: v for (k, v) in self.tags.iteritems() if k in f.net.params['popTagsCopiedToCells']} # copy all pop tags to cell tags, except those that are pop-specific
cellTags['xnorm'] = randLocs[i,0] # calculate x location (um)
cellTags['ynorm'] = randLocs[i,1] # calculate x location (um)
cellTags['znorm'] = randLocs[i,2] # calculate z location (um)
cellTags['x'] = f.net.params['sizeX'] * randLocs[i,0] # calculate x location (um)
cellTags['y'] = f.net.params['sizeY'] * randLocs[i,1] # calculate x location (um)
cellTags['z'] = f.net.params['sizeZ'] * randLocs[i,2] # calculate z location (um)
if 'propList' not in cellTags: cellTags['propList'] = [] # initalize list of property sets if doesn't exist
cells.append(cellModelClass(gid, cellTags)) # instantiate Cell object
if f.cfg['verbose']:
print('Cell %d/%d (gid=%d) of pop %s, pos=(%2.f, %2.f, %2.f), on node %d, '%(i, self.tags['numCells']-1, gid, self.tags['popLabel'],cellTags['x'], cellTags['ynorm'], cellTags['z'], f.rank))
f.net.lastGid = f.net.lastGid + self.tags['numCells']
return cells
import matplotlib.pylab as pl
import LFPy
import numpy as np
import os
import sys
import neuron
from plotting import plotstuff, simple_plot_2D, plot_cell_compartments, plot_sec
from tools import push_simulation_to_folder, analyze_neuron, find_sec_from_comp
sim_folder = "hay_model/"
LFPy.cell.neuron.load_mechanisms(sim_folder + "/mod")
pl.seed(999)
cellParameters = {
"morphology": sim_folder + "lfpy_version/morphologies/cell1.hoc",
#'rm' : 30000, # membrane resistance
#'cm' : 1.0, # membrane capacitance
"Ra": 80, # axial resistance
"v_init": -80, # initial crossmembrane potential
#'e_pas' : -90, # reversal potential passive mechs
#'passive' : True, # switch on passive mechs
#'nsegs_method' : 'lambda_f',# method for setting number of segments,
#'lambda_f' : 100, # segments are isopotential at this frequency
"timeres_NEURON": 2 ** -4, # dt of LFP and NEURON simulation.
"timeres_python": 2 ** -4,
"tstartms": -50, # start time, recorders start at t=0
"tstopms": 120, # stop time of simulation
"custom_code": [sim_folder + "lfpy_version/custom_codes.hoc", sim_folder + "lfpy_version/biophys3.hoc"],
# will if given list of files run this file
}
# Synaptic parameters taken from Hendrickson et al 2011
import matplotlib.pylab as pl
import numpy as np
import os
import sys
#seed for random generation
pl.seed(0)
def find_sec_from_comp(cell, comp):
name_list = []
for sec in cell.allseclist:
name_list.append(str(sec.name()))
for name in name_list:
comps = cell.get_idx_section(name)
if comp in comps:
print comp, name
return name
def push_simulation_to_folder(save_to_folder, data_from_folder):
print "Copying all simulation results from %s " %data_from_folder\
+"and simulation file to folder %s." % save_to_folder
try:
os.mkdir(save_to_folder)
except(OSError):
print "Result folder already exists. Overwriting..."
os.system('cp %s/* %s' %(data_from_folder, save_to_folder))
def find_amplitude_at_comp(signal_with_spike, debug=False):
n_compartments = len(signal_with_spike[:,0])
samps = {7} # Number of valid samps per sequence
stride = {8} # hop size
iSNR = {9:.3} # inverse signal to noise ratio. basically, volume
# of noise added to input
desired_sr = {10} # signals are resampled to this rate
mu = {11} # quantization level
C = {12} # number of chunks
drop_prob = {13} # Dropout probability for pre-output layer
VAL = {14} # Whether to have validation or not
""".format(
seed, num_filts, K, D, dil_reg, reg_out, batch_size, samps,
stride, iSNR, desired_sr, mu, C, drop_prob, VAL
)
print(TXT)
P.seed(seed)
# Getting the data
x1, sr = read("new/ajay1.wav")
y1, sr = read("new/anh_synced_ajay1.wav")
y1 = y1[:len(x1)]
x1 = resample_poly(x1, desired_sr, sr)
vx1 = P.sqrt((x1**2).mean())
x1 = x1 / vx1 * .05
y1 = resample_poly(y1, desired_sr, sr)
vy1 = P.sqrt((y1**2).mean())
y1 = y1 / vy1 * .05
sr = desired_sr
x2, sr = read("new/ajay2.wav")
y2, sr = read("new/anh_synced_ajay2.wav")
