import os
import sys
from neuromllite.NetworkGenerator import check_to_generate_or_run
os.chdir("..")
sys.path.append(".")
sys.argv.append("-jnmlnrn")
from Example7 import generate
sim, net = generate()
check_to_generate_or_run(sys.argv, sim)
def generate(reference,
only_areas_matching=None,
only_ids_matching=None,
include_contra=False,
include_connections=True,
include_detailed_cells=False):
colors = {}
centres = {}
pop_ids = []
used_ids = {}
names = {}
areas = {}
with open('nature13186-s2_1.csv', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
for w in reader:
print w
if w[0] != 'id':
short = w[3].replace(', ','_')
name = w[4].strip('"')
names[short]=name
with open('nature13186-s2_2.csv', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
for w in reader:
print w
if w[0] != 'ID':
short = w[2].replace(', ','_')
area = w[4].strip('"')
areas[short]=area
for n in names: print('%s: \t%s'%(n,names[n]))
for a in areas: print('%s: \t%s'%(a,areas[a]))
#exit()
################################################################################
### Build a new network
net = Network(id=reference)
net.notes = "NOTE: this is only a quick demo!! Do not use it for your research assuming an accurate conversion of the source data!!! "
#cell = Cell(id='dummycell', pynn_cell='IF_cond_alpha')
#cell.parameters = { "tau_refrac":5, "i_offset":.1 }
cell = Cell(id='dummycell', neuroml2_source_file='passiveSingleCompCell.cell.nml')
net.cells.append(cell)
net.synapses.append(Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='cond_alpha',
parameters={'e_rev':-10, 'tau_syn':2}))
'''
r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=1000,height=100,depth=1000)
net.regions.append(r1)
default_cell = Cell(id='L23PyrRS', neuroml2_source_file='TestSmall/L23PyrRS.cell.nml')
net.cells.append(default_cell)
p0 = Population(id='pop0', size=5, component=default_cell.id, properties={'color':'0 .8 0'})
net.populations.append(p0)
net.populations[0].random_layout = RandomLayout(region=r1.id)'''
detailed_cells = ['AA0289'] if include_detailed_cells else []
for dc in detailed_cells:
ll = SingleLocation()
ll.location = Location(x=0,y=0,z=0)
orig_file='%s_active.cell.nml'%dc
new_ref, new_cell_file = get_oriented_cell(orig_file, math.pi,math.pi/2, 5500, 5300, 700)
print("Translated %s to %s"%(orig_file, new_cell_file))
mo = Cell(id=dc, neuroml2_source_file='%s_active.cell.nml'%dc)
net.cells.append(mo)
p1 = Population(id='pop_%s'%dc,
size=1,
component=mo.id,
properties={'color':'.8 0 0'})
p1.single_location=ll
#net.populations.append(p1)
mo = Cell(id=new_ref, neuroml2_source_file=new_cell_file)
net.cells.append(mo)
p1 = Population(id='pop_%s'%new_ref,
size=1,
component=new_ref,
properties={'color':'0 0.8 0'})
p1.single_location=ll
net.populations.append(p1)
f = open('ABA12.tsv')
for l in f:
w = l.split()
print w
pre_id = w[0].replace('-','_').replace('/','_')
if pre_id != '[0]':
match = False
if only_ids_matching==None:
match = True
else:
for i in only_ids_matching:
if i=='*' or i in pre_id:
match = True
if match:
scale = 1000
x0 = float(w[2])*scale
all = [ (pre_id, x0)]
if include_contra:
all = [ (pre_id, x0), ('CONTRA_%s'%pre_id, x0*-1)]
for a in all:
id = a[0]
x = a[1]
centres[id] = (x,float(w[3])*scale,float(w[4])*scale)
colors[id] = w[1]
repl = id
name = names[w[0]]
short_name3 = w[0][:3]
short_name4 = w[0][:4]
short_name5 = w[0][:5]
short_name6 = w[0][:6]
short_name7 = w[0][:7]
if w[0] in areas:
area = areas[w[0]]
elif short_name7 in areas:
area = areas[short_name7]
elif short_name6 in areas:
area = areas[short_name6]
elif short_name5 in areas:
area = areas[short_name5]
elif short_name4 in areas:
area = areas[short_name4]
elif short_name3 in areas:
area = areas[short_name3]
else:
area = '???'
match = False
if only_areas_matching==None:
match = True
else:
for a in only_areas_matching:
if a in area:
match = True
if match:
p = centres[repl]
used_ids[id] = '_%s'%repl if repl[0].isdigit() else repl
region_name = name.split(',')[0].replace(' ','_')
region_name = used_ids[id]
r = RectangularRegion(id=region_name, x=p[0],y=p[1],z=p[2],width=1,height=1,depth=1)
net.regions.append(r)
color = '.8 .8 .8'
if 'Thalamus' in area:
color = '.3 .3 .3'
if 'Isocortex' in area:
color = occ.L23_PRINCIPAL_CELL
if 'bulb' in area:
color = '0 0 1'
if 'Cerebe' in area:
color = '.6 .6 .6'
#if 'Hippocampal' in area:
# color = occ.L6_PRINCIPAL_CELL
if '1' in id:
color = occ.THALAMUS_2
if '2_3' in id:
color = occ.L23_PRINCIPAL_CELL
if '4' in id:
color = occ.L4_PRINCIPAL_CELL
if '5' in id:
color = occ.L5_PRINCIPAL_CELL
if '6' in id:
color = occ.L6_PRINCIPAL_CELL
p0 = Population(id=used_ids[id],
size=1,
component=cell.id,
properties={'color':'%s'%(color),
'radius':50,
'name':name,
'area':area},
random_layout = RandomLayout(region=r.id))
net.populations.append(p0)
pop_ids.append(id)
#print centres.keys()
if include_connections:
with open('nature13186-s4_W_ipsi.csv', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
indices = {}
for w in reader:
#print w
if w[0]=='ROOT':
for i in range(len(w)):
indices[i]=w[i]
print indices
else:
pre = w[0]
for i in range(len(w)):
if i!=0:
weight = float(w[i])
if weight>0:
post = indices[i]
print('Connection %s -> %s of %s'%(pre, post, weight))
if weight>0.0:
if pre in used_ids and post in used_ids:
print('Adding conn from %s -> %s of %s'%(pre, post, weight))
################################################################################
### Add a projection
net.projections.append(Projection(id='proj_%s_%s'%(used_ids[pre],used_ids[post]),
presynaptic=used_ids[pre],
postsynaptic=used_ids[post],
synapse='ampa',
weight=weight,
random_connectivity=RandomConnectivity(probability=1)))
from neuromllite import Simulation
#print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
sim = Simulation(id='Sim_%s'%net.id,
network=new_file,
duration='1000',
dt='0.025',
recordTraces={'all':'*'})
################################################################################
### Export to some formats
### Try:
### python Example1.py -graph2
from neuromllite.NetworkGenerator import check_to_generate_or_run
import sys
check_to_generate_or_run(sys.argv, sim)
if fln>0.0:
net.projections.append(Projection(id='proj_%s_%s'%(src,tgt),
presynaptic=src,
postsynaptic=tgt,
synapse='ampa',
weight=fln))
#net.projections[0].random_connectivity=RandomConnectivity(probability=0.5)
print(net)
net.id = 'TestNetwork'
print(net.to_json())
new_file = net.to_json_file('Example1_%s.json'%net.id)
################################################################################
### Export to some formats
### Try:
### python Example1.py -graph2
from neuromllite.NetworkGenerator import check_to_generate_or_run
from neuromllite import Simulation
import sys
check_to_generate_or_run(sys.argv, Simulation(id='SimExample1',network=new_file))
)
)
net.synapses.append(
Synapse(
id="ampa", neuroml2_source_file="../../../examples/test_files/ampa.synapse.nml"
)
)
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Use a handler which just prints info on positions, etc.
def_handler = DefaultNetworkHandler()
generate_network(net, def_handler)
################################################################################
### Export to some formats, e.g. try:
### python Example2.py -graph2
from neuromllite.NetworkGenerator import check_to_generate_or_run
from neuromllite import Simulation
import sys
check_to_generate_or_run(sys.argv, Simulation(id="Sim%s" % net.id, network=new_file))
heatmap_all=False,
show_plot_already=False,
peak_threshold=0)
report = ps.run()
ps.print_report()
# ps.plotLines('weightInput','average_last_1percent',save_figure_to='average_last_1percent.png')
#ps.plotLines('weightInput','mean_spike_frequency',save_figure_to='mean_spike_frequency.png')
#ps.plotLines('rate','sspPop[0]/spike:mean_spike_frequency',save_figure_to='mean_spike_frequency.png')
#ps.plotLines('rate','sspPop[0]/spike:mean_spike_frequency',second_param='seed',save_figure_to='mean_spike_frequency.png')
#ps.plotLines('rate','sspPop[0]/spike:mean_spike_frequency',second_param='dt',save_figure_to='mean_spike_frequency.png')
ps.plotLines('rateHz','sgpPop[0]/spike:mean_spike_frequency',second_param='seed',save_figure_to='mean_spike_frequency_sgp.png')
#ps.plotLines('periodms','sgPop[0]/spike:mean_spike_frequency',second_param='seed',save_figure_to='mean_spike_frequency_sg.png')
import matplotlib.pyplot as plt
plt.show()
else:
sim, net = generate()
################################################################################
### Run in some simulators
import sys
check_to_generate_or_run(sys.argv, sim)
def read_graphml(filename, color_map=[]):
parser = GraphMLParser()
g = parser.parse(filename)
g.set_root('n0')
print dir(g)
id = '%s_%s'%(filename.split('.')[0],filename.split('.')[1])
net = Network(id=id)
net.notes = "NeuroMLlite conversion of %s from https://www.neurodata.io/project/connectomes/"%filename
#net.parameters = {}
dummy_cell = Cell(id='testcell', pynn_cell='IF_cond_alpha')
dummy_cell.parameters = { "tau_refrac":5, "i_offset":0 }
net.cells.append(dummy_cell)
net.synapses.append(Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='cond_alpha',
parameters={'e_rev':0, 'tau_syn':2}))
for node in g.nodes():
info = ''
for a in node.attributes():
info+=node.attr[a].value+'; '
if len(info)>2: info = info[:-4]
color = '%s %s %s'%(random.random(),random.random(),random.random()) \
if not info in color_map else color_map[info]
print('> Node: %s (%s), color: %s'%(node.id, info, color))
p0 = Population(id=node.id,
size=1,
component=dummy_cell.id,
properties={'color':color})
net.populations.append(p0)
for edge in g.edges():
#print dir(edge)
#print edge.attributes()
src = edge.node1.id
tgt = edge.node2.id
weight = float(str(edge.attr['e_weight'].value)) if 'e_weight' in edge.attr else 1
#print('>> Edge from %s -> %s, weight %s'%(src, tgt, weight))
net.projections.append(Projection(id='proj_%s_%s'%(src,tgt),
presynaptic=src,
postsynaptic=tgt,
synapse='ampa',
weight=weight,
random_connectivity=RandomConnectivity(probability=1)))
#g.show()
#print(net)
#print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
duration=1000
dt = 0.1
sim = Simulation(id='Sim%s'%net.id,
network=new_file,
duration=duration,
dt=dt,
recordRates={'all':'*'})
check_to_generate_or_run(sys.argv, sim)
