def import_swc(swcs):
db = Database(db_name="postgresql://hal08.g-node.pri/lehmann", exec_role="morphjokey_admin", exec_path="morphjokey")
swc_parser = SwcParser()
for swc in swcs:
print(swc)
m_swc = swc_parser.parse(swc)
db.store(m_swc)
print("start to commit data according to '%s'." % (swc))
tic = time.time()
db.session.commit()
toc = time.time()
print("Done. It took %s seconds." % (toc - tic))
# -*- coding: utf-8 -*-
from mrj.io.swc import SwcParser
from mrj.io.database import Database
db = Database(
db_name='postgresql://hal08.g-node.pri/lehmann',
exec_role='morphjokey_admin',
exec_path='morphjokey')
swc_parser = SwcParser(db)
m = swc_parser.parse('../../../Data/example_morph.swc')
#print( m )
#print( m._compartments[0])
db.store( m )
#db.create_subtree( m )
# morphology_key mapped via sqlalchemy:
print m.morphology_key
m = db.load_morphology( m.morphology_key )
print( m ) # contains ALL auto calculated data from database
#print( m._compartments[0])
#print "\n\n\ncompartments as dicts:"
#for c in m._compartments:
# print c.info.__dict__
#a = db.session.execute("SELECT compartment_id,compartment_parent_id,children FROM morphjokey.mrj_v_compartments_children WHERE morphology_key = 150;")
#b = a.fetchall()
# -*- coding: utf-8 -*-
'''
@author: stransky
'''
from mrj.io.swc import SwcParser
from mp.model.synapse import Synapse
from mp.model.synapse import getSynapticActivity
from mrj.model.experiment import IClamp
from mrj.model.experiment import Experiment
from mrj.model.experiment import Neuron_passive_parameter
from mrj.model.experiment import plot
swc_parser = SwcParser()
m_swc = swc_parser.parse("../../../Data/test.swc") #slow swc_parser.parse("../../Data/MSO_Neurone/P09/ohne_axon_SkeletonTree_0015_2-1G.swc")
m_swc.neuron_create() # initialisiert die Synapse in NEURON
compartment = m_swc.compartments[5]
s = Synapse(compartment, 0.5, syntimes=getSynapticActivity(f=200, duration=5, sigma=0, fireing_rate=1, delay=0))#TODO: ??delta_time > 5
s.neuron_create()
clamp = IClamp(compartment=compartment, position=0.5, delay=1,amplitude=0,duration=3)#VClamp
neuron_passive_parameter = \
Neuron_passive_parameter(Ra=1, g=0.004, e=-60, nseg=10)
f = Experiment(m_swc, clamp, neuron_passive_parameter,
description='passive channels')
f.neuron_create()
r_in, tau_eff = f.run_simulation(duration=20,dt=0.001)
tau_eff_fit = f.tau_fit()
