plotting.remove_axis_junk(ax)
# draw the first NCELLS cells in each population
NCELLS = 20
CWD = PSET.CWD
CELLPATH = PSET.CELLPATH
# cell positions and rotations file:
f = h5py.File(os.path.join(PSET.OUTPUTPATH,
'cell_positions_and_rotations.h5'), 'r')
for i, data in enumerate(PSET.populationParameters):
NRN = data["me_type"]
os.chdir(os.path.join(CWD, CELLPATH, NRN))
for j in range(NCELLS):
try:
cell = NetworkCell(**PSET.cellParameters[NRN])
cell.set_pos(
x=f[NRN]['x'][j],
y=f[NRN]['y'][j],
z=f[NRN]['z'][j])
cell.set_rotation(
x=f[NRN]['x_rot'][j],
y=f[NRN]['y_rot'][j],
z=f[NRN]['z_rot'][j])
zips = []
for x, z in cell.get_idx_polygons(projection=('x', 'z')):
zips.append(list(zip(x, z)))
polycol = PolyCollection(zips,
edgecolors=colors[i],
def plot_m_types(ax,
PSET,
colors,
section=['dend', 'apic'],
spacing=300,
linewidths=0.05):
'''draw comparison plot of each individual morphology'''
CWD = PSET.CWD
CELLPATH = PSET.CELLPATH
n_segs = []
areas = []
for i, data in enumerate(PSET.populationParameters):
NRN = data["me_type"]
os.chdir(os.path.join(CWD, CELLPATH, NRN))
cell = NetworkCell(**PSET.cellParameters[NRN])
cell.set_pos(x=i * spacing, y=0, z=data['pop_args']['loc'])
cell.set_rotation(x=np.pi / 2)
print(NRN, cell.zstart.min(), cell.zmid.min(), cell.zend.min(),
cell.zstart.max(), cell.zmid.max(), cell.zend.max())
n_segs += [cell.totnsegs]
areas += [cell.area[cell.get_idx(section)].sum()]
zips = []
for x, z in cell.get_idx_polygons(projection=('x', 'z')):
zips.append(list(zip(x, z)))
polycol = PolyCollection(
zips,
edgecolors=colors[i],
linewidths=linewidths,
facecolors=colors[i],
label=NRN,
)
ax.add_collection(polycol)
os.chdir(CWD)
axis = ax.axis(ax.axis('tight'))
# draw lines showing the layer boundaries
ax.hlines(np.r_[0., -PSET.layer_data['thickness'].cumsum()][:4],
axis[0],
axis[1] - 300,
'k',
lw=0.5)
ax.hlines(np.r_[0., -PSET.layer_data['thickness'].cumsum()][4:],
axis[0],
axis[1],
'k',
lw=0.5)
# annotate hlines with values
for z in np.r_[0., -PSET.layer_data['thickness'].cumsum()]:
ax.text(axis[0],
z,
r'$z={}$'.format(int(z)) + '$\mu$m',
ha='right',
va='center')
ax.set_yticks(PSET.layer_data['center'])
ax.set_yticklabels(PSET.layer_data['layer'])
ax.set_xticks(np.arange(PSET.populationParameters.size) * spacing)
ax.set_xticklabels(PSET.populationParameters['m_type'],
rotation='vertical')
ax.axis(ax.axis('equal'))
ax.set_title('m-types')
neuron.h("forall delete_section()")
return n_segs, areas
"""
import matplotlib.pyplot as plt
from LFPy import NetworkCell, StimIntElectrode
import neuron
# class NetworkCell parameters
cellParameters = dict(
morphology='BallAndStick.hoc',
templatefile='BallAndStickTemplate.hoc',
templatename='BallAndStickTemplate',
templateargs=None,
passive=False,
v_init=-65
)
# create cell instance
cell = NetworkCell(
tstart=0, tstop=100,
**cellParameters
)
# stimulus device
iclamp = StimIntElectrode(
cell=cell,
idx=0,
pptype='IClamp',
amp=0.5,
dur=80,
delay=10,
record_current=True
)
# run simulation
cell.simulate()
plt.subplot(2,1,1)