nrn.mustache

#!/usr/bin/ipython -i

from neuron import *
from nrn import *

def create_comp(name='soma'):
    comp = h.Section(name)

    comp.nseg = {{geometry.nseg}}
    comp.L = {{geometry.L}}
    comp.diam = {{geometry.diam}}

    {{#mod_mechanisms}}
    comp.insert('{{name}}')
    {{#gbar.name}}
    comp.{{gbar.name}} = {{gbar.val}}
    {{/gbar.name}}
    {{^gbar.name}}
    comp.gbar_{{name}} = {{gbar}}
    {{/gbar.name}}
    {{#ion}}
    comp.e{{ion}} = {{rev}}
    {{/ion}}
    {{^ion}}
    comp.e{{name}} = {{rev}}
    {{/ion}}
    {{/mod_mechanisms}}

    {{#nml_mechanisms}}
    comp.insert('{{name}}')
    {{#gbar.name}}
    comp.{{gbar.name}} = {{gbar.val}}
    {{/gbar.name}}
    {{^gbar.name}}
    comp.gbar_{{name}} = {{gbar}}
    {{/gbar.name}}
    {{#ion}}
    comp.e{{ion}} = {{rev}}
    {{/ion}}
    {{^ion}}
    comp.e{{name}} = {{rev}}
    {{/ion}}
    {{/nml_mechanisms}}

    {{#passive}}
    comp.insert('{{name}}')
    comp.g{{ion}} = {{g}}
    comp.e{{ion}} = {{rev}}
    {{/passive}}

    {{#reversals}}
    {{/reversals}}

    return comp

def get_next_hex_color():
    from random import randint
    
    return "#%06x" %randint(0,0xFFFFFF)


def plot_timeseries(vdict, varlist):
    import matplotlib.pyplot as plt
    t = vdict['t']
    v = vdict['v']
    
    fig = plt.figure(figsize=(10, 12))

    ax = fig.add_subplot(2, 1, 1)
    ax.plot(t, v, 'k-', linewidth=2)
    ax.set_title('membrane potential')
    ax.set_xlim([0, {{run.total}}])

    ax2 = fig.add_subplot(2, 1, 2)
    for n in range(1, len(varlist)):
        ax2.plot(t, vdict[varlist[n]], label=varlist[n], color=get_next_hex_color(), linewidth=2)
    ax2.set_title('recorded gating variables')
    ax2.set_xlim([0, {{run.total}}])
    ax2.set_xlabel('Time [ms]')
    ax2.set_ylim([0, 1])
    ax2.legend()

    plt.show()


def create_dumps(section, varlist):
    recordings = {n: h.Vector() for n in varlist}

    for (vn, v) in recordings.iteritems():
        v.record(section(0.5).__getattribute__('_ref_' + vn))

    recordings['t'] = h.Vector()
    recordings['t'].record(h._ref_t)
    return recordings


def dump_to_file(vdict, varlist, fname='sccct.nrn.dat'):
    from numpy import savetxt, array

    vnames = ['t'] + varlist
    X = array([vdict[x].to_python() for x in vnames]).T
    savetxt(fname, X)


def run(tstop=10, dt=0.001):
    h.dt = dt
    h.finitialize({{run.v0}})
    h.fcurrent()
    h.frecord_init()
    while h.t < tstop:
        h.fadvance()

comp = create_comp('soma')
h.celsius = {{run.temp}}

inputs = []

{{#inputs}}
stim = h.IClamp(0.5, sec=comp)
stim.delay = {{delay}}
stim.dur = {{dur}}
stim.amp = {{amp}}
inputs.append(stim)

{{/inputs}}


varlist = {{record}}
ds = create_dumps(comp, varlist)

run({{run.total}}, {{run.dt}})

{{#plot}}
plot_timeseries(ds, varlist)
{{/plot}}
dump_to_file(ds, varlist)