def define_geometry(icc): icc.diam = 4.4 #7.136*2 icc.L = 65.92 icc.nseg = 1 icc.Ra = 50 #50 icc.cm = .025*(10**5)/h.area(0.5,sec=icc)
def area_in_list(self,seclist):
area = 0
for sec in seclist:
if h.ismembrane('chanrhod',sec = sec):
for seg in sec:
area += h.area(seg.x, sec=sec) # um2
return area
def set_seg_props(self):
"""Set segment properties which are invariant for all cell using this morphology"""
seg_type = []
seg_area = []
seg_x = []
seg_dist = []
seg_length = []
h.distance(sec=self.hobj.soma[0]) # measure distance relative to the soma
for sec in self.hobj.all:
fullsecname = sec.name()
sec_type = fullsecname.split(".")[1][:4] # get sec name type without the cell name
sec_type_swc = self.sec_type_swc[sec_type] # convert to swc code
for seg in sec:
seg_area.append(h.area(seg.x))
seg_x.append(seg.x)
seg_length.append(sec.L/sec.nseg)
seg_type.append(sec_type_swc) # record section type in a list
seg_dist.append(h.distance(seg.x)) # distance to the center of the segment
self.seg_prop = {}
self.seg_prop['type'] = np.array(seg_type)
self.seg_prop['area'] = np.array(seg_area)
self.seg_prop['x'] = np.array(seg_x)
self.seg_prop['dist'] = np.array(seg_dist)
self.seg_prop['length'] = np.array(seg_length)
self.seg_prop['dist0'] = self.seg_prop['dist'] - self.seg_prop['length']/2
self.seg_prop['dist1'] = self.seg_prop['dist'] + self.seg_prop['length']/2
def compute_total_area(self):
self.total_area = 0
for sec in h.allsec():
for seg in sec:
self.total_area += h.area(seg.x, sec)
if DEBUG:
print('Total area: %.0f um^2.' % self.total_area)
def retrieve_coordinate(sec):
sec.push()
x, y, z, d = [],[],[],[]
area = 0
tot_points = 0
connect_next = False
for i in range(int(h.n3d())):
present = False
x_i = h.x3d(i)
y_i = h.y3d(i)
z_i = h.z3d(i)
d_i = h.diam3d(i)
a_i = h.area(0.5)
if x_i in x:
ind = len(x) - 1 - x[::-1].index(x_i) # Getting the index of last value
if y_i == y[ind]:
if z_i == z[ind]:
present = True
if not present:
k =(x_i, y_i, z_i)
x.append(x_i)
y.append(y_i)
z.append(z_i)
d.append(d_i)
area += np.sum(a_i)
h.pop_section()
#adding num 3d points per section
n3dpoints[sec.name()] = [np.array(x),np.array(y),np.array(z),np.array(d)]
return (np.array(x),np.array(y),np.array(z),np.array(d),area)
def retrieve_coordinate(sec):
sec.push()
x, y, z, d = [], [], [], []
area = 0
tot_points = 0
connect_next = False
for i in range(int(h.n3d())):
present = False
x_i = h.x3d(i)
y_i = h.y3d(i)
z_i = h.z3d(i)
d_i = h.diam3d(i)
a_i = h.area(0.5)
if x_i in x:
ind = len(x) - 1 - x[::-1].index(
x_i) # Getting the index of last value
if y_i == y[ind]:
if z_i == z[ind]:
present = True
if not present:
k = (x_i, y_i, z_i)
x.append(x_i)
y.append(y_i)
z.append(z_i)
d.append(d_i)
area += np.sum(a_i)
h.pop_section()
#adding num 3d points per section
n3dpoints[sec.name()] = [
np.array(x), np.array(y),
np.array(z), np.array(d)
]
return (np.array(x), np.array(y), np.array(z), np.array(d), area)
def illuminated_area_in_list(self,seclist,Tx_threshold = 0.001):
area = 0
for sec in seclist:
if h.ismembrane('chanrhod',sec = sec):
for seg in sec:
if seg.Tx_chanrhod>Tx_threshold:
area += h.area(seg.x, sec=sec) # um2
return area
def channels_in_list(self,seclist):
channels = 0
for sec in seclist:
if h.ismembrane('chanrhod',sec = sec):
for seg in sec:
rho = seg.channel_density_chanrhod/1e8 # 1/cm2 --> 1/um2
area = h.area(seg.x, sec=sec) # um2
n = rho * area
channels += n
return channels
def radiant_power(self,seclist):
""" Find the radiant power integrated over all sections in seclist
"""
p = 0
for sec in seclist:
for seg in sec:
area = h.area(seg.x, sec=sec)*1e-8 # um2 --> cm2
#p += (area * self.amplitude * sec.Tx_chanrhod) # Add section's radiant power
p += (area * self.amplitude * sec.Tx_chanrhod)
return p
def get_icat(self):
""" Determine the total amount of channelrhodopsin current in the cell
"""
icat = 0
for sec in h.allsec():
if h.ismembrane('chanrhod', sec=sec):
for seg in sec:
i = seg.icat_chanrhod # (mA/cm2)
area = h.area(seg.x, sec=sec) / 1e8 # cm2
icat += area * i # mA
return icat
def section_area(section):
x_list = []
a = 0
for seg in section:
x = seg.x
x_list.append(x)
for x in x_list:
a += h.area(x, sec=section)
return a
def define_geometry(icc):
icc.diam = 4.4 #7.136*2
icc.L = 65.92
icc.nseg = 1
icc.Ra = 50 #50
area = 4 * 100 * 8 + 2 * 20 * 8
icc.cm = .025 * (10**5) / h.area(0.5, sec=icc)
print('capacitance=', icc.cm)
def set_seg_props(self):
"""Set segment properties which are invariant for all cell using this morphology"""
seg_type = []
seg_area = []
seg_x = []
seg_dist = []
seg_length = []
h.distance(
sec=self.hobj.soma[0]) # measure distance relative to the soma
for sec in self.hobj.all:
fullsecname = sec.name()
sec_type = fullsecname.split(
".")[1][:4] # get sec name type without the cell name
sec_type_swc = self.sec_type_swc[sec_type] # convert to swc code
for seg in sec:
seg_area.append(h.area(seg.x))
seg_x.append(seg.x)
seg_length.append(sec.L / sec.nseg)
seg_type.append(sec_type_swc) # record section type in a list
seg_dist.append(h.distance(
seg.x)) # distance to the center of the segment
'''
for sec in self.hobj.all:
sec_name = sec.name()
segs = [s for s in sec]
seg_len = sec.L / sec.nseg
print sec_name, sec.L, len(segs),':',
for s in segs:
seg_dist = h.distance(s.x)
print '[{} - {} - {}]'.format(seg_dist-seg_len/2, seg_dist, seg_dist+seg_len/2),
# print h.distance(s.x),
#print s.x,
print '({})'.format(sec.L/sec.nseg)
#print ' ',
#for s in segs:
# print '[{} - {}]'.format()
#print sec.name(), '-', len([s for s in sec])
'''
self.seg_prop = {}
self.seg_prop['type'] = np.array(seg_type)
self.seg_prop['area'] = np.array(seg_area)
self.seg_prop['x'] = np.array(seg_x)
self.seg_prop['dist'] = np.array(seg_dist)
self.seg_prop['length'] = np.array(seg_length)
self.seg_prop[
'dist0'] = self.seg_prop['dist'] - self.seg_prop['length'] / 2
self.seg_prop[
'dist1'] = self.seg_prop['dist'] + self.seg_prop['length'] / 2
def open_channels_in_list(self,seclist):
open_channels = 0
for sec in seclist:
if h.ismembrane('chanrhod', sec = sec):
for seg in sec:
rho = seg.channel_density_chanrhod/1e8 # 1/cm2 --> 1/um2
area = h.area(seg.x, sec=sec) # um2
try:
f_open = seg.o2_chanrhod + seg.o1_chanrhod # open fraction # 4 state model
except:
f_open = seg.o1_chanrhod # open fraction # 3 state model
n = f_open * rho * area
open_channels += n
return open_channels
def define_biophysics(self):
#Function: define_biophysics
#Input: self
#Process: set parameters
#Output: neurons with biophysics
self.totalarea = 0
for sec in self.all: # 'all' exists in parent object.
sec.Ra = 1e-5 # Axial resistance in Ohm * cm
sec.cm = 1 # Membrane capacitance in micro Farads / cm^2
for seg in sec:
self.totalarea += h.area(seg.x)
# Dendrite passive
for d in self.dendlist:
d.insert('leak')
def _do_memb_scales(self):
if not self._scale_by_area:
areas = numpy.ones(len(areas))
else:
areas = numpy.array(
sum([
list(self._regions[0]._geometry.volumes1d(sec)
for sec in self._regions[0].secs)
], []))
neuron_areas = []
for sec in self._regions[0].secs:
neuron_areas += [
h.area((i + 0.5) / sec.nseg, sec=sec) for i in xrange(sec.nseg)
]
neuron_areas = numpy.array(neuron_areas)
area_ratios = areas / neuron_areas
# still needs to be multiplied by the valence of each molecule
self._memb_scales = -area_ratios * FARADAY / (10000 *
molecules_per_mM_um3)
def define_biophysics(self):
#Function: define_biophysics
#Input: self
#Process: set parameters
#Output: neurons with biophysics
self.totalarea = 0
for sec in self.all: # 'all' exists in parent object.
sec.Ra = 1e-5 # Axial resistance in Ohm * cm
sec.cm = self.Captot # Membrane capacitance in micro Farads / cm^2
for seg in sec:
self.totalarea += h.area(seg.x)
# Soma passive
self.soma.insert('leak')
self.soma.g_leak = self.condtot
factor2 = (np.float32(seg_count)+1.)/t_segs
sp_name = str(sec.name()).rsplit('.')[-1]+"_"+str(seg_count)
try:
location_dict[sp_name] = [(x[-1]*factor1)+(x[0]*(1-factor1)),
(y[-1]*factor1)+(y[0]*(1-factor1)),
(z[-1]*factor1)+(z[0]*(1-factor1)),
(d[-1]*factor1)+(d[0]*(1-factor1)),
(x[-1]*factor2)+(x[0]*(1-factor2)),
(y[-1]*factor2)+(y[0]*(1-factor2)),
(z[-1]*factor2)+(z[0]*(1-factor2)),
(d[-1]*factor2)+(d[0]*(1-factor2)), ]
except IndexError: #no location specified
location_dict[sp_name] = location_dict['soma[0]_0']#[0,0,0,0,0,0,0,0]
i_dict[sp_name] = [ina, ik, ica, i_cap, i_pas, ih]
sec_dict[sp_name] = sec
area_dict[sp_name] = h.area( ((2.*seg_count)+1)/(2.*sec.nseg) )
#Default simulation parameters
thresh = -20
spikezaehler = h.NetCon(h.L5PCtemplate[0].soma[0](0.5)._ref_v, None, thresh, 0, 0)
spikes = h.Vector()
spikezaehler.record(spikes)
t_vec = h.Vector()
t_vec.record(h._ref_t)
#RUN SIMULATION
h.init()
h.run()
#CALCULATIONS BEFORE PLOTTING
t_vec = np.array(t_vec)
def main(config, template_path, output_path, forest_path, populations,
distance_bin_size, io_size, chunk_size, value_chunk_size, cache_size,
verbose):
"""
:param config:
:param template_path:
:param forest_path:
:param populations:
:param io_size:
:param chunk_size:
:param value_chunk_size:
:param cache_size:
"""
utils.config_logging(verbose)
logger = utils.get_script_logger(script_name)
comm = MPI.COMM_WORLD
rank = comm.rank
env = Env(comm=MPI.COMM_WORLD,
config_file=config,
template_paths=template_path)
configure_hoc_env(env)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
if output_path is None:
output_path = forest_path
if rank == 0:
if not os.path.isfile(output_path):
input_file = h5py.File(forest_path, 'r')
output_file = h5py.File(output_path, 'w')
input_file.copy('/H5Types', output_file)
input_file.close()
output_file.close()
comm.barrier()
layers = env.layers
layer_idx_dict = {
layers[layer_name]: layer_name
for layer_name in ['GCL', 'IML', 'MML', 'OML', 'Hilus']
}
(pop_ranges, _) = read_population_ranges(forest_path, comm=comm)
start_time = time.time()
for population in populations:
logger.info('Rank %i population: %s' % (rank, population))
count = 0
(population_start, _) = pop_ranges[population]
template_class = load_cell_template(env,
population,
bcast_template=True)
measures_dict = {}
for gid, morph_dict in NeuroH5TreeGen(forest_path,
population,
io_size=io_size,
comm=comm,
topology=True):
if gid is not None:
logger.info('Rank %i gid: %i' % (rank, gid))
cell = cells.make_neurotree_cell(template_class,
neurotree_dict=morph_dict,
gid=gid)
secnodes_dict = morph_dict['section_topology']['nodes']
apicalidx = set(cell.apicalidx)
basalidx = set(cell.basalidx)
dendrite_area_dict = {k: 0.0 for k in layer_idx_dict}
dendrite_length_dict = {k: 0.0 for k in layer_idx_dict}
dendrite_distances = []
dendrite_diams = []
for (i, sec) in enumerate(cell.sections):
if (i in apicalidx) or (i in basalidx):
secnodes = secnodes_dict[i]
for seg in sec.allseg():
L = seg.sec.L
nseg = seg.sec.nseg
seg_l = L / nseg
seg_area = h.area(seg.x)
seg_diam = seg.diam
seg_distance = get_distance_to_node(
cell,
list(cell.soma)[0], seg.sec, seg.x)
dendrite_diams.append(seg_diam)
dendrite_distances.append(seg_distance)
layer = synapses.get_node_attribute(
'layer', morph_dict, seg.sec, secnodes, seg.x)
dendrite_length_dict[layer] += seg_l
dendrite_area_dict[layer] += seg_area
dendrite_distance_array = np.asarray(dendrite_distances)
dendrite_diam_array = np.asarray(dendrite_diams)
dendrite_distance_bin_range = int(
((np.max(dendrite_distance_array)) -
np.min(dendrite_distance_array)) / distance_bin_size) + 1
dendrite_distance_counts, dendrite_distance_edges = np.histogram(
dendrite_distance_array,
bins=dendrite_distance_bin_range,
density=False)
dendrite_diam_sums, _ = np.histogram(
dendrite_distance_array,
weights=dendrite_diam_array,
bins=dendrite_distance_bin_range,
density=False)
dendrite_mean_diam_hist = np.zeros_like(dendrite_diam_sums)
np.divide(dendrite_diam_sums,
dendrite_distance_counts,
where=dendrite_distance_counts > 0,
out=dendrite_mean_diam_hist)
dendrite_area_per_layer = np.asarray([
dendrite_area_dict[k]
for k in sorted(dendrite_area_dict.keys())
],
dtype=np.float32)
dendrite_length_per_layer = np.asarray([
dendrite_length_dict[k]
for k in sorted(dendrite_length_dict.keys())
],
dtype=np.float32)
measures_dict[gid] = {
'dendrite_distance_hist_edges':
np.asarray(dendrite_distance_edges, dtype=np.float32),
'dendrite_distance_counts':
np.asarray(dendrite_distance_counts, dtype=np.int32),
'dendrite_mean_diam_hist':
np.asarray(dendrite_mean_diam_hist, dtype=np.float32),
'dendrite_area_per_layer':
dendrite_area_per_layer,
'dendrite_length_per_layer':
dendrite_length_per_layer
}
del cell
count += 1
else:
logger.info('Rank %i gid is None' % rank)
append_cell_attributes(output_path,
population,
measures_dict,
namespace='Tree Measurements',
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size,
cache_size=cache_size)
MPI.Finalize()
def Constructing_the_ball_and_tree(params, cables,
spiking_mech=False, nmda_on=False, Ca_spikes_on=False, HH_on=False):
# list of excitatory stuff
exc_synapses, exc_netcons, exc_spike_trains, exc_Ks = [], [], [], []
# list of inhibitory stuff
inh_synapses, inh_netcons, inh_spike_trains, inh_Ks = [], [], [], []
# area list
area_lists = []
area_tot = 0
## --- CREATING THE NEURON
for level in range(len(cables)):
# list of excitatory stuff per level
exc_synapse, exc_netcon, exc_spike_train, exc_K = [], [], [], []
# list of inhibitory stuff per level
inh_synapse, inh_netcon, inh_spike_train, inh_K = [], [], [], []
area_list = []
for comp in range(max(1,2**(level-1))):
nrn('create cable_'+str(int(level))+'_'+\
str(int(comp)))
exec('section = nrn.cable_'+str(level)+'_'+str(comp))
## --- geometric properties
section.L = cables[level]['L']*1e6
section.diam = cables[level]['D']*1e6
section.nseg = cables[level]['NSEG']
## --- passive properties
section.Ra = params['Ra']*1e2
section.cm = params['cm']*1e2
section.insert('pas')
section.g_pas = params['g_pas']*1e-4
section.e_pas = params['El']*1e3
if comp==0 and HH_on:
section.insert('hh3Larkum2009')
if (comp==1 or comp==2) and Ca_spikes_on:
section.insert('scaLarkum2009')
section.gbar_scaLarkum2009=10
# section.insert('kdrLarkum2009')
# section.insert('kcaLarkum2009')
# section.gbar_kcaLarkum2009=30
section.insert('cad2Larkum2009')
section.insert('it2Larkum2009')
# section.gcabar_it2Larkum2009=0.000
# section.insert('hh3Larkum2009')
# section.gkbar_hh3Larkum2009=0
# section.gkbar2_hh3Larkum2009=0.0001
# section.gnabar_hh3Larkum2009=0.01
# section.insert('kapLarkum2009')
# section.gkabar_kapLarkum2009=0.003
# section.insert('kmLarkum2009')
# section.gbar_kmLarkum2009=0
# section.gbar_kdrLarkum2009=0
if level>=1: # if not soma
# in NEURON : connect daughter, mother branch
nrn('connect cable_'+str(int(level))+'_'+\
str(int(comp))+'(0), '+\
'cable_'+str(int(level-1))+\
'_'+str(int(comp/2.))+'(1)')
## --- SPREADING THE SYNAPSES (bis)
for seg in section:
tree_fraction = np.max([0,float(seg.x)/(len(cables)-1)+float(level-1)/(len(cables)-1)])
if tree_fraction>params['fraction_for_L_prox']:
# print 'strengthen synapse !'
Ftau = 1. # removed params !
Fq = params['factor_for_distal_synapses_weight']
else:
Ftau = 1.
Fq = 1.
# in each segment, we insert an excitatory synapse
if nmda_on:
syn = nrn.my_glutamate(seg.x, sec=section)
syn.tau_ampa, syn.e = Ftau*params['Te']*1e3, params['Ee']*1e3
syn.gmax = 1e9*params['Qe'] # nS in my_glutamate.mod
else:
syn = nrn.ExpSyn(seg.x, sec=section)
syn.tau, syn.e = Ftau*params['Te']*1e3, params['Ee']*1e3
exc_synapse.append(syn)
netcon = nrn.NetCon(nrn.nil, syn)
netcon.weight[0] = Fq*params['Qe']*1e6
exc_netcon.append(netcon)
exc_K.append(cables[level]['Ke_per_seg'])
exc_spike_train.append([])
syn = nrn.ExpSyn(seg.x, sec=section)
syn.tau, syn.e = Ftau*params['Ti']*1e3, params['Ei']*1e3
inh_synapse.append(syn)
inh_K.append(cables[level]['Ki_per_seg'])
inh_spike_train.append([])
netcon = nrn.NetCon(nrn.nil, syn)
netcon.weight[0] = Fq*params['Qi']*1e6
inh_netcon.append(netcon)
# then just the area to check
area_tot+=nrn.area(seg.x, sec=section)
area_list.append(nrn.area(seg.x, sec=section))
exc_synapses.append(exc_synapse)
exc_netcons.append(exc_netcon)
exc_Ks.append(exc_K)
exc_spike_trains.append(exc_spike_train)
inh_synapses.append(inh_synapse)
inh_netcons.append(inh_netcon)
inh_Ks.append(inh_K)
inh_spike_trains.append(inh_spike_train)
area_lists.append(area_list)
# ## --- spiking properties
if spiking_mech: # only at soma if we want it !
nrn('cable_0_0 insert hh')
# + spikes recording !
counter = nrn.APCount(0.5, sec=nrn.cable_0_0)
spkout = nrn.Vector()
counter.thresh = -30
counter.record(spkout)
else:
spkout = np.zeros(0)
print("=======================================")
print(" --- checking if the neuron is created ")
nrn.topology()
print(("with the total surface : ", area_tot))
print("=======================================")
return exc_synapses, exc_netcons, exc_Ks, exc_spike_trains,\
inh_synapses, inh_netcons, inh_Ks, inh_spike_trains, area_lists, spkout
def _do_memb_scales(self, cur_map):
if not self._scale_by_area:
areas = numpy.ones(len(areas))
else:
# TODO: simplify this expression
areas = numpy.array(
itertools.chain.from_iterable([
list(self._regions[0]._geometry.volumes1d(sec)
for sec in self._regions[0].secs)
]))
neuron_areas = []
for sec in self._regions[0].secs:
neuron_areas += [
h.area((i + 0.5) / sec.nseg, sec=sec) for i in range(sec.nseg)
]
neuron_areas = numpy.array(neuron_areas)
# area_ratios is usually a vector of 1s
area_ratios = areas / neuron_areas
# still needs to be multiplied by the valence of each molecule
self._memb_scales = -area_ratios * FARADAY / (10000 *
molecules_per_mM_um3)
#print area_ratios
#print self._memb_scales
#import sys
#sys.exit()
# since self._memb_scales is only used to compute currents as seen by the rest of NEURON,
# we only use NEURON's areas
#self._memb_scales = volume * molecules_per_mM_um3 / areas
if self._membrane_flux:
# TODO: don't assume/require always inside/outside on one side...
# if no nrn_region specified, then (make so that) no contribution
# to membrane flux
source_regions = [s()._region()._nrn_region for s in self._sources]
dest_regions = [d()._region()._nrn_region for d in self._dests]
if 'i' in source_regions and 'o' not in source_regions and 'i' not in dest_regions:
inside = -1 #'source'
elif 'o' in source_regions and 'i' not in source_regions and 'o' not in dest_regions:
inside = 1 # 'dest'
elif 'i' in dest_regions and 'o' not in dest_regions and 'i' not in source_regions:
inside = 1 # 'dest'
elif 'o' in dest_regions and 'i' not in dest_regions and 'o' not in source_regions:
inside = -1 # 'source'
else:
raise RxDException(
'unable to identify which side of reaction is inside (hope to remove the need for this'
)
# dereference the species to get the true species if it's actually a SpeciesOnRegion
sources = [s()._species() for s in self._sources]
dests = [d()._species() for d in self._dests]
if self._membrane_flux:
if any(s in dests for s in sources) or any(d in sources
for d in dests):
# TODO: remove this limitation
raise RxDException(
'current fluxes do not yet support same species on both sides of reaction'
)
# TODO: make so don't need multiplicity (just do in one pass)
# TODO: this needs changed when I switch to allowing multiple sides on the left/right (e.g. simplified Na/K exchanger)
self._cur_charges = tuple(
[-inside * s.charge for s in sources if s.name is not None] +
[inside * s.charge for s in dests if s.name is not None])
self._net_charges = sum(self._cur_charges)
self._cur_ptrs = []
self._cur_mapped = []
for sec in self._regions[0].secs:
for i in range(sec.nseg):
local_ptrs = []
local_mapped = []
for sp in itertools.chain(self._sources, self._dests):
spname = sp()._species().name
if spname is not None:
name = '_ref_i%s' % (spname)
seg = sec((i + 0.5) / sec.nseg)
local_ptrs.append(seg.__getattribute__(name))
uberlocal_map = [None, None]
if spname + 'i' in cur_map:
uberlocal_map[0] = cur_map[spname + 'i'][seg]
if spname + 'o' in cur_map:
uberlocal_map[1] = cur_map[spname + 'o'][seg]
local_mapped.append(uberlocal_map)
self._cur_ptrs.append(tuple(local_ptrs))
self._cur_mapped.append(tuple(local_mapped))
def calc_area(self):
self.total_area = 0
self.n = 0
for sect in self.all_sec:
self.total_area += h.area(0.5, sec=sect)
self.n += 1
def insert_mechanisms(icc):
icc.insert('pas')
icc.g_pas = .01
icc(0.5).g_pas = 0
icc.insert('Na')
icc.nai = nai_
icc.nao = nao_
icc.ena = ena_
icc.G_Na_Na = 0 #20
icc.tau_f_Na_Na = tau_f_Na
icc.tau_d_Na_Na = tau_d_Na
icc.insert('nscc')
icc.G_NSCC_nscc = 0 #12.15
icc.tau_NSCC_nscc = tau_NSCC
icc.insert('ERG')
icc.G_ERG_ERG = 0 #2.5
icc.tau_ERG_ERG = tau_ERG
icc.ki = ki_ #23
icc.ko = ko_ #23
icc.ek = ek_ #23
icc.insert('bk')
icc.G_bk_bk = 0 #23+T_correction_BK
icc.insert('Kb')
icc.G_Kb_Kb = 0 #.15
icc.insert('kv11')
icc.G_Kv11_kv11 = 0 #6.3
icc.tau_d_kv11_kv11 = tau_d_kv11
icc.tau_f_kv11_kv11 = tau_f_kv11
icc.insert('vddr')
icc.G_Ca_VDDR_vddr = 0 #3
icc.cao = 2.5
icc.tau_d_VDDR_vddr = tau_d_VDDR
icc.tau_f_VDDR_vddr = tau_f_VDDR
icc.insert('ltype')
icc.G_Ca_Ltype_ltype = 0 #2
icc.tau_f_Ltype_ltype = tau_f_Ltype
icc.tau_d_Ltype_ltype = tau_d_Ltype
icc.tau_f_Ca_Ltype_ltype = tau_f_Ca_Ltype
icc.insert('pmca')
icc.J_max_PMCA_pmca = 0 #0.088464
time_corr = 10**(-3)
icc.insert('concyto')
J_max_leak = 0.01 * time_corr
icc.J_max_leak_concyto = 0 #0.01(mM/s)
icc.Vol_concyto = pi * ((icc.diam / 2)**2) * icc.L
icc.insert('conpu')
icc.J_max_leak_conpu = 0 #0.01(mM/s)
icc.Jmax_serca_conpu = 0 # 1.8333
icc.J_ERleak_conpu = 0 # 1.666667
icc.Jmax_IP3_conpu = 0 #50000 (1/s)
icc.Jmax_NaCa_conpu = 0 #0.05(mM/s)
icc.Jmax_uni_conpu = 0 #5000 mM/s
icc.insert('ano1')
icc.g_Ano1_ano1 = 0 #20
icc.cli = cli_
icc.clo = clo_
icc.ecl = ecl_
icc.insert('cacl')
icc.G_Cacl_cacl = 0 #10.1
icc.tau_act_Cacl_cacl = tau_act_Cacl
active_sites = [0.5]
a = 1 / (10 * h.area(0.5))
for i in active_sites:
icc(
i
).G_Na_Na = a * 20 #.0022 #this is the tuned value #20 - total conductance
icc(i).G_NSCC_nscc = a * 0 #12.15
icc(i).G_ERG_ERG = a * 2.5 #2.5
icc(i).G_bk_bk = a * (23 + T_correction_BK) #23+T_correction_BK
icc(i).G_Kb_Kb = a * 0.15 #.15
icc(i).G_Kv11_kv11 = a * 6.3 #6.3
icc(i).G_Ca_VDDR_vddr = a * 3 #3
icc(i).G_Ca_Ltype_ltype = a * 2 #2
icc(i).J_max_PMCA_pmca = 0.088464 * time_corr #0.088464
icc(i).J_max_leak_concyto = J_max_leak #0.01(mM/s)
icc(i).J_max_leak_conpu = J_max_leak #0.01(mM/s)
icc(i).Jmax_serca_conpu = 1.8333 * time_corr # 1.8333
icc(i).J_ERleak_conpu = 1.666667 * time_corr # 1.666667
icc(i).Jmax_IP3_conpu = 50000 * time_corr #50000 (1/s)
icc(i).Jmax_NaCa_conpu = 0.05 * time_corr #0.05(mM/s)
icc(i).Jmax_uni_conpu = 5000 * time_corr #5000 mM/s
icc(i).g_Ano1_ano1 = a * 20 #20
icc(i).G_Cacl_cacl = a * 10.1 #10.1
# plt.show() #print(icc.tau_f_Na_Na, tau_f_Na_Na) # plt.plot(t,ina, label = 'i_na') # plt.plot(t,ik_ERG, label = 'i_erg') # plt.plot(t,ik_Kb, label = 'i_kb') # plt.plot(t,ik_bk, label = 'ik_bk') # plt.plot(t,ik_kv11, label = 'i_kv11') # plt.plot(t,ica_vddr, label = 'i_vddr') # plt.plot(t,cai) # plt.plot(t,capui) # plt.plot(t,caeri) # plt.plot(ecl) # plt.plot(eca) # plt.plot(t,ik_bk, label = 'i_na') # plt.plot(t,ica_ltype, label = 'i_ltype') # plt.plot(t,icl_cacl, label = 'i_cacl') # plt.plot(t,icl_ano1, label = 'i_ano1') # plt.plot(t,i_nscc, label = 'i_nscc') # plt.plot(t,ica_pmca, label = 'i_pmca') plt.show() print(h.area(0.5))
"""
Topology - connectivity between sections
"""
dend.connect(soma(1))
"""
Geometry - 3D location of the sections
"""
# Surface area of cylinder is 2*pi*r*h (sealed ends are implicit).
# Here we make a square cylinder in that the diameter
# is equal to the height, so diam = h. ==> Area = 4*pi*r^2
# We want a soma of 500 microns squared:
# r^2 = 500/(4*pi) ==> r = 6.2078, diam = 12.6157
soma.L = soma.diam = 12.6157
dend.L = 200 # microns
dend.diam = 1 # microns
print "Surface area of soma=", h.area(0.5, sec=soma)
shape_window = h.PlotShape()
shape_window.exec_menu('Show Diam')
"""
Biophysics - ionic channels and membrane properties of the sections
"""
for sec in h.allsec():
sec.Ra = 100
sec.cm = 1
# Active HH current in soma
soma.insert('hh')
soma.gnabar_hh = 0.12
soma.gkbar_hh = 0.036
soma.gl_hh = 0.0003
def setup_lfp_coeffs(self):
ex, ey, ez = self.epoint
for pop_name in self.celltypes:
lfp_ids = self.lfp_ids[pop_name]
lfp_types = self.lfp_types[pop_name]
lfp_coeffs = self.lfp_coeffs[pop_name]
for i in range(0, int(lfp_ids.size())):
## Iterates over all cells chosen for the LFP computation
gid = lfp_ids.x[i]
cell = self.pc.gid2cell(gid)
## Iterates over each compartment of the cell
for sec in list(cell.all):
if h.ismembrane('extracellular', sec=sec):
nn = sec.n3d()
xx = h.Vector(nn)
yy = h.Vector(nn)
zz = h.Vector(nn)
ll = h.Vector(nn)
for ii in range(0, nn):
xx.x[ii] = sec.x3d(ii)
yy.x[ii] = sec.y3d(ii)
zz.x[ii] = sec.z3d(ii)
ll.x[ii] = sec.arc3d(ii)
xint = h.Vector(sec.nseg + 2)
yint = h.Vector(sec.nseg + 2)
zint = h.Vector(sec.nseg + 2)
interpxyz(nn, sec.nseg, xx, yy, zz, ll, xint, yint, zint)
j = 0
sx0 = xint.x[0]
sy0 = yint.x[0]
sz0 = zint.x[0]
for seg in sec:
sx = xint.x[j]
sy = yint.x[j]
sz = zint.x[j]
## l = L/nseg is compartment length
## rd is the perpendicular distance from the electrode to a line through the compartment
## ld is longitudinal distance along this line from the electrode to one end of the compartment
## sd = l - ld is longitudinal distance to the other end of the compartment
l = float(sec.L) / sec.nseg
rd = math.sqrt((ex - sx) * (ex - sx) + (ey - sy) * (ey - sy) + (ez - sz) * (ez - sz))
ld = math.sqrt((sx - sx0) * (sx - sx0) + (sy - sy0) * (sy - sy0) + (sz - sz0) * (sz - sz0))
sd = l - ld
k = 0.0001 * h.area(seg.x) * (self.rho / (4.0 * math.pi * l)) * abs(math.log(
((math.sqrt(ld * ld + rd * rd) - ld) / (math.sqrt(sd * sd + rd * rd) - sd))))
if math.isnan(k):
k = 0.
## Distal cell
if (lfp_types.x[i] == 2):
k = (1.0 / self.fdst) * k
##printf ("host %d: npole_lfp: gid = %d i = %d j = %d r = %g h = %g k = %g\n", pc.id, gid, i, j, r, h, k)
lfp_coeffs.o(i).x[j] = k
j = j + 1
import sys
sys.path.append('../../')
from neuron import h
from neuron import gui
from templates.mli.stellate import Stellate
from templates.synapse.synapse import Synapse
import numpy as np
import random as rnd
stls = []
stls.append(Stellate(np.array([0., 0., 0.]), model_type='C'))
stls[0].soma.push()
print h.area(.5) * 1e-8, ' cm2'
vc = h.VClamp(.5, sec=stls[0].soma)
vc.amp[0] = -80 # mV
vc.dur[0] = 100
vc.amp[1] = -90 # mV
vc.dur[1] = 300
vc.amp[2] = -80 # mV
vc.dur[2] = 100
vclampi = h.Vector()
vclampi.record(vc._ref_i)
npf = 1
cells.append(cell)
#cells[a].soma.gcaN_clarke = cells[a].soma.gcaN_clarke + step
#cells[a].soma.gcaL_clarke = cells[a].soma.gcaL_clarke + step
#cells[a].soma.gcak_clarke = cells[a].soma.gcak_clarke + step
#cells[a].soma.gnapbar_clarke = cells[a].soma.gnapbar_clarke + step
#cells[a].soma.tau_mc_clarke = cells[a].soma.tau_mc_clarke + step
#cells[a].soma.tau_hc_clarke = cells[a].soma.tau_hc_clarke + step
#cells[a].dap_nc_.weight[0] = cells[a].dap_nc_.weight[0] +step
#cells[a].soma.gkrect_clarke = cells[a].soma.gkrect_clarke + step
#cells[a].soma.tau_mp_bar_clarke = cells[a].soma.tau_mp_bar_clarke + step
#cells[a].soma.tau_n_bar_clarke = cells[a].soma.tau_n_bar_clarke + step
#cells[a].soma.gnabar_clarke = cells[a].soma.gnabar_clarke + step
#shape_window = h.PlotShape()
#shape_window.exec_menu('Show Diam')
cellSurface = h.area(0.5, sec=cells[0].soma)
## Make a netstim
stim = h.NetStim() # Make a new stimulator
stim.interval = 150
stim.noise = 0
stim.number = 1
stim.start = 9200
## Attach it to a synapse at syn_loc
ncstim = h.NetCon(stim, cells[0].syn_I)
ncstim.delay = 0
ncstim.weight[0] = 1.75e-3 # NetCon weight is a vector.
#Stims and clamps
#stim = h.IClamp(cells[0].dend(syn_loc))
#stim.delay = 200
location_dict[sp_name] = [
(x[-1] * factor1) + (x[0] * (1 - factor1)),
(y[-1] * factor1) + (y[0] * (1 - factor1)),
(z[-1] * factor1) + (z[0] * (1 - factor1)),
(d[-1] * factor1) + (d[0] * (1 - factor1)),
(x[-1] * factor2) + (x[0] * (1 - factor2)),
(y[-1] * factor2) + (y[0] * (1 - factor2)),
(z[-1] * factor2) + (z[0] * (1 - factor2)),
(d[-1] * factor2) + (d[0] * (1 - factor2)),
]
except IndexError: #no location specified
location_dict[sp_name] = location_dict[
'soma[0]_0'] #[0,0,0,0,0,0,0,0]
i_dict[sp_name] = [ina, ik, ica, i_cap, i_pas, ih]
sec_dict[sp_name] = sec
area_dict[sp_name] = h.area(((2. * seg_count) + 1) / (2. * sec.nseg))
#Default simulation parameters
thresh = -20
spikezaehler = h.NetCon(h.L5PCtemplate[0].soma[0](0.5)._ref_v, None, thresh, 0,
0)
spikes = h.Vector()
spikezaehler.record(spikes)
t_vec = h.Vector()
t_vec.record(h._ref_t)
#RUN SIMULATION
h.init()
h.run()
#CALCULATIONS BEFORE PLOTTING
soma = h.Section(name="soma")
dir(soma)
soma.insert("pas")
soma.nseg = 3
h.psection(sec=soma)
dend = h.Section(name="dend")
dend.connect(soma, 1)
dend.nseg = 10
print(h.topology())
soma.L = soma.diam = 12.6157
dend.L = 200
dend.diam = 1
h.area(0.1, sec=dend)
pas = soma(0.5).pas
pas.g = 0.002
print("pas: "******"\nALL SECTIONS:")
for sec in h.allsec():
print(sec.name())
for seg in sec:
print("\t" + str(seg.x))
for mech in seg:
print("\t\t" + mech.name())
v_e.record(icc(0.6)._ref_v) v_e1.record(icc(0.7)._ref_v) v_e2.record(icc(.8)._ref_v) v_e3.record(icc(.9)._ref_v) v_e4.record(icc(1)._ref_v) vclamp = h.SEClamp(icc(0.5)) vclamp.dur1 = tstop vclamp.amp1 = -50.0 vclamp.rs = .00001 h.v_init = -80 run_and_record(icc, *variables) t = np.array(t.to_python()) ina = h.area(0.5) * 10 * np.array(ina.to_python()) ik = h.area(0.5) * 10 * np.array(ik.to_python()) ik_ERG = h.area(0.5) * 10 * np.array(ik_ERG.to_python()) ik_Kb = h.area(0.5) * 10 * np.array(ik_Kb.to_python()) ik_kv11 = h.area(0.5) * 10 * np.array(ik_kv11.to_python()) ica_vddr = h.area(0.5) * 10 * np.array(ica_vddr.to_python()) ik_bk = h.area(0.5) * 10 * np.array(ik_bk.to_python()) ica_ltype = h.area(0.5) * 10 * np.array(ica_ltype.to_python()) icl_cacl = h.area(0.5) * 10 * np.array(icl_cacl.to_python()) icl_ano1 = h.area(0.5) * 10 * np.array(icl_ano1.to_python()) cai_vddr = np.array(cai.to_python()) capui = np.array(capui.to_python()) # ik = ik.to_python() # ina_new = [i * (h.area(0.5)*10) for i in ina]
N = 3
r = 50 # Radius of cell locations from origin (0,0,0) in microns
for i in range(N):
cell = ClarkeRelay()
# When cells are created, the soma location is at (0,0,0) and
# the dendrite extends along the X-axis.
# First, at the origin, rotate about Z.
cell.rotateZ(i*2*pi/N)
# Then reposition
x_loc = sin(i * 2 * pi / N) * r
y_loc = cos(i * 2 * pi / N) * r
cell.set_position(x_loc, y_loc, 0)
cells.append(cell)
cellSurface = h.area(0.5, sec = cells[0].soma)
h.celsius = 37
#shape_window = h.PlotShape()
#shape_window.exec_menu('Show Diam')
#stim = h.NetStim() # Make a new stimulator
# Attach it to a synapse in the middle of the dendrite
# of the first cell in the network. (Named 'syn_' to avoid
# being overwritten with the 'syn' var assigned later.)
#syn_ = h.ExpSyn(cells[0].dend(0.5))
#syn_.tau = 10
#stim.number = 1
#stim.start = 9
#ncstim = h.NetCon(stim, syn_)
def _do_memb_scales(self, cur_map):
"""Set up self._memb_scales and cur_map."""
if not self._scale_by_area:
areas = numpy.ones(len(areas))
else:
volumes = numpy.concatenate([list(self._regions[0]._geometry.volumes1d(sec) for sec in self._regions[0].secs)])
neuron_areas = []
for sec in self._regions[0].secs:
neuron_areas += [h.area((i + 0.5) / sec.nseg, sec=sec) for i in xrange(sec.nseg)]
neuron_areas = numpy.array(neuron_areas)
# area_ratios is usually a vector of 1s
area_ratios = volumes / neuron_areas
# still needs to be multiplied by the valence of each molecule
self._memb_scales = -area_ratios * FARADAY / (10000 * molecules_per_mM_um3)
#print area_ratios
#print self._memb_scales
#import sys
#sys.exit()
# since self._memb_scales is only used to compute currents as seen by the rest of NEURON,
# we only use NEURON's areas
#self._memb_scales = volume * molecules_per_mM_um3 / areas
if self._membrane_flux:
# TODO: don't assume/require always inside/outside on one side...
# if no nrn_region specified, then (make so that) no contribution
# to membrane flux
source_regions = [s()._region()._nrn_region for s in self._sources]
dest_regions = [d()._region()._nrn_region for d in self._dests]
if 'i' in source_regions and 'o' not in source_regions and 'i' not in dest_regions:
inside = -1 #'source'
elif 'o' in source_regions and 'i' not in source_regions and 'o' not in dest_regions:
inside = 1 # 'dest'
elif 'i' in dest_regions and 'o' not in dest_regions and 'i' not in source_regions:
inside = 1 # 'dest'
elif 'o' in dest_regions and 'i' not in dest_regions and 'o' not in source_regions:
inside = -1 # 'source'
else:
raise RxDException('unable to identify which side of reaction is inside (hope to remove the need for this')
# dereference the species to get the true species if it's actually a SpeciesOnRegion
sources = [s()._species() for s in self._sources]
dests = [d()._species() for d in self._dests]
if self._membrane_flux:
if any(s in dests for s in sources) or any(d in sources for d in dests):
# TODO: remove this limitation
raise RxDException('current fluxes do not yet support same species on both sides of reaction')
# TODO: make so don't need multiplicity (just do in one pass)
# TODO: this needs changed when I switch to allowing multiple sides on the left/right (e.g. simplified Na/K exchanger)
self._cur_charges = tuple([-inside * s.charge for s in sources if s.name is not None] + [inside * s.charge for s in dests if s.name is not None])
self._net_charges = sum(self._cur_charges)
self._cur_ptrs = []
self._cur_mapped = []
for sec in self._regions[0].secs:
for i in xrange(sec.nseg):
local_ptrs = []
local_mapped = []
for sp in itertools.chain(self._sources, self._dests):
spname = sp()._species().name
if spname is not None:
name = '_ref_i%s' % (spname)
seg = sec((i + 0.5) / sec.nseg)
local_ptrs.append(seg.__getattribute__(name))
uberlocal_map = [None, None]
if spname + 'i' in cur_map:
uberlocal_map[0] = cur_map[spname + 'i'][seg]
# if spname + 'o' in cur_map:
# uberlocal_map[1] = cur_map[spname + 'o'][seg]
local_mapped.append(uberlocal_map)
self._cur_ptrs.append(tuple(local_ptrs))
self._cur_mapped.append(tuple(local_mapped))
def compute_section_area(section):
a = 0.
for segment in section:
a += h.area(segment.x, sec=section)
return a
def test_direct_memory_transfer():
#h('''create soma''')
cell = h.rinzelnrn()
#h.psection()
#dir(h)
#h.soma.L=5.6419
#h.soma.diam=5.6419
#h.soma.insert("rinzelnrn")
gc = 2.1
pp = 0.5
cell.soma.Ra = 1
cell.dend.Ra = 1
global_Ra = (1e-6 / (gc / pp *
(h.area(0.5) * 1e-8) * 1e-3)) / (2 * h.ri(0.5))
cell.soma.Ra = global_Ra
cell.soma.cm = 3
cell.dend.Ra = global_Ra
cell.dend.cm = 3
# soma
cell.soma.insert("pas")
cell.soma.insert("kdr")
cell.soma.insert("nafPR")
cell.soma.gmax_nafPR = 30e-3
cell.soma.gmax_kdr = 15e-3
cell.soma.g_pas = 0.1e-3
cell.soma.e_pas = -60
# dend
cell.dend.insert("pas")
cell.dend.insert("rcadecay")
cell.dend.insert("cal")
cell.dend.insert("kcRT03")
cell.dend.insert("rkq")
cell.dend.g_pas = 0.1e-3
cell.dend.e_pas = -60
cell.dend.phi_rcadecay = 130
cell.dend.gmax_cal = 00010e-3
cell.dend.erev_cal = 80
cell.dend.gmax_kcRT03 = 00015e-3
cell.dend.gmax_rkq = 0000.8e-3
cell.dend.ek = -75
ic = h.IClamp(cell.soma(.5))
ic.delay = .5
ic.dur = 0.1
ic.amp = 0.3
#for testing external mod file
#h.soma.insert("hh")
h.cvode.use_fast_imem(1)
h.cvode.cache_efficient(1)
h.tstop = 50
h.celsius = 37.0
h.v_init = -60
v = h.Vector()
v.record(cell.soma(.5)._ref_v, sec=cell.soma)
i_mem = h.Vector()
i_mem.record(cell.soma(.5)._ref_i_membrane_, sec=cell.soma)
tv = h.Vector()
tv.record(h._ref_t, sec=cell.soma)
h.run()
vstd = v.cl()
tvstd = tv.cl()
i_memstd = i_mem.cl()
# Save current (after run) value to compare with transfer back from coreneuron
#tran_std = [h.t, cell.soma(.5).v, cell.soma(.5).hh.m]
from neuron import coreneuron
coreneuron.enable = True
pc = h.ParallelContext()
pc.set_maxstep(10)
h.stdinit()
pc.psolve(h.tstop)
#tran = [h.t, cell.soma(.5).v, cell.soma(.5).hh.m]
assert (tv.eq(tvstd))
#assert(v.cl().sub(vstd).abs().max() < 1e-10)
assert (i_mem.cl().sub(i_memstd).abs().max() < 1e-10)
#assert(h.Vector(tran_std).sub(h.Vector(tran)).abs().max() < 1e-10)
f = open('v.dat', 'w')
for i in range(tv.size()):
print('{} {}'.format(tv[i], v[i]), file=open("v.dat", "a"))
h.quit()
def segment_area(input_segment):
x = input_segment.x
a = h.area(x, sec=input_segment.section)
return a
def calc_area(self):
self.total_area = 0
self.n = 0
for sect in self.all_sec:
self.total_area += h.area(0.5,sec=sect)
self.n+=1
def main(config, template_path, output_path, forest_path, populations, io_size,
chunk_size, value_chunk_size, cache_size, verbose):
"""
:param config:
:param template_path:
:param forest_path:
:param populations:
:param io_size:
:param chunk_size:
:param value_chunk_size:
:param cache_size:
"""
utils.config_logging(verbose)
logger = utils.get_script_logger(script_name)
comm = MPI.COMM_WORLD
rank = comm.rank
env = Env(comm=MPI.COMM_WORLD,
config_file=config,
template_paths=template_path)
h('objref nil, pc, templatePaths')
h.load_file("nrngui.hoc")
h.load_file("./templates/Value.hoc")
h.xopen("./lib.hoc")
h.pc = h.ParallelContext()
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
h.templatePaths = h.List()
for path in env.templatePaths:
h.templatePaths.append(h.Value(1, path))
if output_path is None:
output_path = forest_path
if rank == 0:
if not os.path.isfile(output_path):
input_file = h5py.File(forest_path, 'r')
output_file = h5py.File(output_path, 'w')
input_file.copy('/H5Types', output_file)
input_file.close()
output_file.close()
comm.barrier()
(pop_ranges, _) = read_population_ranges(forest_path, comm=comm)
start_time = time.time()
for population in populations:
logger.info('Rank %i population: %s' % (rank, population))
count = 0
(population_start, _) = pop_ranges[population]
template_name = env.celltypes[population]['template']
h.find_template(h.pc, h.templatePaths, template_name)
template_class = eval('h.%s' % template_name)
measures_dict = {}
for gid, morph_dict in NeuroH5TreeGen(forest_path,
population,
io_size=io_size,
comm=comm,
topology=True):
if gid is not None:
logger.info('Rank %i gid: %i' % (rank, gid))
cell = cells.make_neurotree_cell(template_class,
neurotree_dict=morph_dict,
gid=gid)
secnodes_dict = morph_dict['section_topology']['nodes']
apicalidx = set(cell.apicalidx)
basalidx = set(cell.basalidx)
dendrite_area_dict = {k + 1: 0.0 for k in range(0, 4)}
dendrite_length_dict = {k + 1: 0.0 for k in range(0, 4)}
for (i, sec) in enumerate(cell.sections):
if (i in apicalidx) or (i in basalidx):
secnodes = secnodes_dict[i]
prev_layer = None
for seg in sec.allseg():
L = seg.sec.L
nseg = seg.sec.nseg
seg_l = old_div(L, nseg)
seg_area = h.area(seg.x)
layer = cells.get_node_attribute(
'layer', morph_dict, seg.sec, secnodes, seg.x)
layer = layer if layer > 0 else (
prev_layer if prev_layer is not None else 1)
prev_layer = layer
dendrite_length_dict[layer] += seg_l
dendrite_area_dict[layer] += seg_area
measures_dict[gid] = { 'dendrite_area': np.asarray([ dendrite_area_dict[k] for k in sorted(dendrite_area_dict.keys()) ], dtype=np.float32), \
'dendrite_length': np.asarray([ dendrite_length_dict[k] for k in sorted(dendrite_length_dict.keys()) ], dtype=np.float32) }
del cell
count += 1
else:
logger.info('Rank %i gid is None' % rank)
append_cell_attributes(output_path,
population,
measures_dict,
namespace='Tree Measurements',
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size,
cache_size=cache_size)
MPI.Finalize()
def photons(self,sec):
section_area = h.area(0.5,sec=sec) # um2
section_intensity = self.intensity(sec) # photons/ms cm2