def mkcells(gidinfo):
timeit()
for gid in gidinfo:
x, y, z = gidinfo[gid]
cell = h.Cell()
gidinfo[gid] = CellInfo(cell)
# cell shape is actually an arc and area is fastidious with respect
# to all 6 sides. But length
# treated as line distance between org points (interior corners
# in circumferential direction. Set diam so area is correct including
# end areas.
cell.soma.pt3dclear()
cell.soma.pt3dadd(x, y, z, 1.)
ilayer, icircle, ipt = gid2org(gid)
x1, y1, z1 = xyz(ilayer, icircle, ipt + 1)
cell.soma.pt3dadd(x1, y1, z1, 1.)
length = cell.soma.L
area = sum(mkgap.cell_side_areas(gid))
diam = area / pi / length
cell.soma.diam = diam
assert (isclose(cell.soma(.5).area(), area, abs_tol=area * 1e-5))
cell.position(x, y, z)
pc.set_gid2node(gid, rank)
nc = cell.connect2target(None)
pc.cell(gid, nc)
x = pc.allreduce(len(gidinfo), 1)
pr("Global number of real cells is %d" % x)
timeit("mkcells")
def mkgaps(gidinfo, gaps):
timeit()
mark = set()
for gapinfo in gaps.values():
gg = (gapinfo.gid1, gapinfo.gid2)
id = gapinfo.id
mkhalfgap(gg[0], gg[1], id, gidinfo, mark)
mkhalfgap(gg[1], gg[0], -id, gidinfo, mark)
pc.setup_transfer()
x = 0
for cell in gidinfo.values():
x += len(cell.gaps)
x = pc.allreduce(x, 1)
pr("Global number of halfgap is %d" % x)
timeit("mkgaps")
def setallgaps(meang, interval, drift):
npurkgap = 0
for gid1, cellinfo in gidinfo.items():
for gid2, gap in cellinfo.gaps.items():
gapinfo = mkgap.gaps[(gid1, gid2) if gid1 < gid2 else (gid2, gid1)]
area = gapinfo.area
g = mkgap.abscond(area, meang)
if is_purkinje_gap(gapinfo.gid1, gapinfo.gid2):
g *= param.purkinje_gap_factor
cellinfo.is_purk = True
npurkgap += 1
gap.meang = g
gap.gmax = g
gap.gmin = g
gap.g = g
gap.rg = interval
gap.drift = drift
npurkgap = pc.allreduce(npurkgap, 1)
pr("number of purkinje gaps is %d" % (npurkgap / 2))
def cellconread():
timeit()
# new Heart-3D paraboloid organization
global ncon, ncell, connections
import cellorg, mkgap
from cellorg import sim_layers, sim_circles
ncell = cellorg.ngid
#old way iterating over all possible cells takes 5.4 seconds
for gid in range(rank, ncell, nhost):
ilayer, icircle, ipt = cellorg.gid2org(gid)
if icircle < cellorg.ncircle[ilayer] - 1:
if cellorg.is_simulated(ilayer, icircle, ipt):
xyz = cellorg.xyz(ilayer, icircle, ipt)
gidinfo[gid] = xyz
'''
#new way iterating only over cells that exist takes
import param as p
for ilayer in sim_layers:
for icircle in sim_circles[ilayer]:
i0 = cellorg.angle2ipt(p.simulation_angledeg[0]*2*pi/360, ilayer, icircle)
i1 = cellorg.angle2ipt(p.simulation_angledeg[1]*2*pi/360, ilayer, icircle)
for ipt in range(i0, i1+1):
if cellorg.is_simulated(ilayer, icircle, ipt):
gid = cellorg.org2gid(ilayer, icircle, ipt)
if gid%nhost == rank:
gidinfo[gid] = cellorg.xyz(ilayer, icircle, ipt)
'''
timeit("gidinfo setup")
for gid in gidinfo:
# because of floating round-off error which may or may not create
# a gap with area close to 0, guarantee gap pairs by only creating
# gaps where gid1 < gid2
mkgap.gaps_for_gid(gid)
n = int(pc.allreduce(n_triang_zero(), 1))
pr("accurate_triang_area calculation returned zero %d times" % n)
timeit("connections determined")
# for parallel, copy gid2 gaps to ranks that need them
mkgap.gaps_gid2_copy()
connections = mkgap.gaps
def ecg_final():
#return
for i in range(nelectrode):
pc.allreduce(ecg_v[i], 1)
def nhost_min(v):
return int(pc.allreduce(min(v), 3))
def nhost_max(v):
return int(pc.allreduce(max(v), 2))
def nhost_sum(v):
return int(pc.allreduce(sum(v), 1))
def nhost_len(v):
return int(pc.allreduce(len(v), 1))
def chk_gran_conn():
model = modeldata.getmodel()
ggids = model.granule_gids
mgids = model.mitral_gids
mgrss = model.mgrss
# for each granule, list of all mgid it connects to and vice versa
g2m = {}
m2g = {}
for mgrs in mgrss.values():
ggid = mgrs.ggid
mgid = mgrs.mgid
if mgrs.gd: # granule exists on this rank
if ggid not in g2m:
g2m[ggid] = []
g2m[ggid].append(mgid)
if mgrs.md: # mitral or mtufted exists on this rank
if mgid not in m2g:
m2g[mgid] = []
m2g[mgid].append(ggid)
a = 'Number of granules that go to mitral and mtufted (regardless of glomerulus)'
na = 0
for ms in g2m.values():
nmit = 0
nmtuft = 0
for mgid in ms:
nmit += 1 if gidfunc.ismitral(mgid) else 0
nmtuft += 1 if gidfunc.ismtufted(mgid) else 0
na += 1 if nmit > 0 and nmtuft > 0 else 0
na = int(pc.allreduce(na, 1))
if pc.id() == 0:
print("%d %s" % (na, a))
a = '[total,imin,imax,iavg,xmin,xmax,xavg] granule connections to mitral'
b = '[total,imin,imax,iavg,xmin,xmax,xavg] granule connections to mtuft'
nab = [[[], [], [], []], [[], [], [], []]]
for ms in g2m.values():
# assume if first is mitral then all are mitrals otherwise mtuft
dat = nab[0] if gidfunc.ismitral(ms[0]) else nab[1]
# want to distinguish intra mitral, distinct mitral, and any mitral
# generate map of distinct:count for that distinct instance
distinct = {}
for d in set(ms):
distinct[d] = 0
for m in ms:
distinct[m] += 1
n_any = len(ms)
n_distinct_m = len(distinct)
n_intra_m_min = min(distinct.values())
n_intra_m_max = max(distinct.values())
dat[0].append(n_any)
dat[1].append(n_distinct_m)
dat[2].append(n_intra_m_min)
dat[3].append(n_intra_m_max)
def nhost_len(v):
return int(pc.allreduce(len(v), 1))
def nhost_sum(v):
return int(pc.allreduce(sum(v), 1))
def nhost_max(v):
return int(pc.allreduce(max(v), 2))
def nhost_min(v):
return int(pc.allreduce(min(v), 3))
def pr0(s):
if pc.id() == 0:
print(s)
def pr(title, dat):
i = 0
pr0(title)
totg = nhost_len(dat[i])
pr0("%d granules of this type" % totg)
pr0("%d total connections" % nhost_sum(dat[i]))
if totg:
pr0("%d min ; %d max ; %g avg" % (nhost_min(
dat[i]), nhost_max(dat[i]), float(nhost_sum(dat[i])) / totg))
pr(a, nab[0])
pr(b, nab[1])
