import neuroml.loaders as loaders
from neuroml import Cell

cell_doc = loaders.NeuroMLLoader.load("SingleCompMuscle.cell.nml")
cell = cell_doc.cells[0]
print('Loaded cell: %s'%cell.id)

segment = cell.morphology.segments[0]
p = segment.proximal
d = segment.distal

print('Cell has a single segment (id: %s, name: %s) consisting of a cylinder from (%s, %s, %s) to (%s, %s, %s)' % 
      (segment.id, segment.name, p.x, p.y, p.z, d.x, d.y, d.z))

membrane_properties = cell.biophysical_properties.membrane_properties
reversal_potentials = {}
densities = {}

for chan_dens in membrane_properties.channel_densities:
    reversal_potentials[chan_dens.id] = chan_dens.erev
    densities[chan_dens.id] = chan_dens.cond_density
    
print("Reversal potentials: %s"%reversal_potentials)
print("Channel densities: %s"%densities)

k_slow = loaders.NeuroMLLoader.load("k_slow.channel.nml").ion_channel[0]
k_fast = loaders.NeuroMLLoader.load("k_fast.channel.nml").ion_channel[0]
ca_boyle = loaders.NeuroMLLoader.load("ca_boyle.channel.nml").ion_channel[0]

for chan in [k_slow, k_fast, ca_boyle]:
    print('Loaded channel: %s'%chan.id)
    for g in chan.gate_hh_tau_infs:
        print("  Gate %s present, instances %i"%(g.id,g.instances))
        if g.time_course:
            print("    Time course: %s, tau: %s"%(g.time_course.type, g.time_course.tau))
        if g.steady_state:
            print("    Steady state: %s, rate: %s, scale: %s, midpoint: %s"%(g.steady_state.type, g.steady_state.rate, g.steady_state.scale, g.steady_state.midpoint))