def step(n, loop_n, save_path, plot=True):
ds.simulate(n)
if plot:
if save_path is False:
ds.plot_neurons(show_nodes=True)
else:
ds.plot_neurons(show_nodes=False, save_path=save_path)
def step(n, loop_n, plot=True):
ds.simulate(n*minute)
if plot:
ds.plot_neurons(show_nodes=True, show=True)
swc_file = ds.get_simulation_id()
# print(swc_file)
return swc_file
if __name__ == '__main__':
num_omp = 1
kernel = {
"seeds": np.random.randint(0, 10000, num_omp).tolist(),
"num_local_threads": num_omp,
"environment_required": False
}
swc_file = lateral_branching(neuron_params)
ds.plot_neurons(show=True)
pop = ds.get_neurons()
n = pop[0]
tree = n.axon.get_tree()
tree.show_dendrogram()
# ~ import neurom
# ~ from neurom import viewer
# ~ asym = []
# ~ num_tips = []
# ~ for n in pop:
# ~ tree = n.axon.get_tree()
# ~ num_tips.append(len(tree.tips))
# ~ nrn = tree.neurom_tree()
def step(n, loop_n, plot=True):
ds.simulate(n)
if plot:
ds.plot_neurons(show_nodes=True, show=False)
# becouse the angle is computed numerically as dy/dx there can
# be some spurious elements arround 10^-10, remove them and
# all the NO Turning zero with np.where
hist_tmp = np.array(hist_tmp)
hist_tmp = hist_tmp[~np.isclose(hist_tmp, 0)]
# make the histogram
ahist, _ = np.histogram(hist_tmp, abins, normed=True)
ax.plot(abins[:-1] + 0.5 * (abins[1] - abins[0]),
ahist,
color=cmap(colors[k]),
label="resol: {}".format(resol))
ax.set_yscale("log", nonposy='clip')
if show_neurons:
ds.plot_neurons(show=False, title=str(resol))
# compute distrib x positions
xs = np.concatenate(structure["growth_cones"])[:, 0]
print(np.average(xs), np.sum(np.isnan(xs)))
count, bins = np.histogram(xs, bins=xbins)
ax2.plot(bins[:-1] + 0.5 * np.diff(bins),
count,
color=cmap(colors[k]),
alpha=0.5,
label="resol: {}".format(resol))
'''
Make, save and show the figure
'''
ax.legend(loc=2, fancybox=True, frameon=True)
neuron_params['growth_cone_model'] = 'default'
neuron_params['position'] = np.random.uniform(-10000, 10000,
(num_neurons, 2))
gids = ds.create_neurons(n=num_neurons,
growth_cone_model='random_walk',
params=neuron_params,
dendrites_params=dendrite_params,
num_neurites=2)
'''
Create recorders
'''
gids_rec = ds.create_recorders(gids, "length", levels="growth_cone")
rec_ngc = ds.create_recorders(gids, "num_growth_cones", levels="neuron")
#~ step(6000, 0, True)
#~ for i in range(10):
#~ print("\nNew step block")
#~ step(2000, 0, True)
for i in range(10):
print("\nNew step block")
step(2000, 0, False)
ds.plot_neurons(show_nodes=True, show=True)
#~ pprint(ds.get_object_properties(gids_rec))
ds.plot_recording(rec_ngc, time_units="minutes")
def step(time, loop_n, plot=True):
ds.simulate(time)
if plot:
ds.plot_neurons(show_nodes=True, show=True)
}
gids = ds.create_neurons(n=num_neurons, num_neurites=1, params=params)
ds.simulate(800)
neurons = ds.structure.NeuronStructure(gids)
tips = np.concatenate(
[neurons["growth_cones"][i] for i in range(num_neurons)])
contained = shape.areas["default_area"].contains_neurons(tips)
fractions.append(np.sum(contained) / float(num_neurons))
lengths.append(neurite_length(gids))
ds.plot_neurons(show=False, title="resol {}".format(resol))
'''
Plot the results
'''
fig, ax = plt.subplots()
ax.plot(resolutions, fractions, ls="-", color="b", alpha=0.8)
# ~ for i, width in enumerate(widths):
# ~ ax.plot(resolutions, fractions[i][:, 1], ls="-", color=colors[i], alpha=0.8, label=str(width))
# ~ ax.legend()
fig.patch.set_alpha(0.)
params = {
"sensing_angle": 70. * deg,
# ~ "filopodia_wall_affinity": 10.*np.sqrt(resol),
# ~ "filopodia_wall_affinity": 10.*resol,
# ~ "filopodia_wall_affinity": 10/np.sqrt(resol),
"filopodia_wall_affinity": 10.,
"proba_down_move": 0.02,
"scale_up_move": 1.,
# ~ "filopodia_wall_affinity": 10./resol,
}
gids = ds.create_neurons(n=num_neurons, num_neurites=1, params=params)
ds.simulate(simtime)
ds.plot_neurons(show=False, title="Resolution: {}".format(resol))
lengths.append(neurite_length(gids))
'''
Plot the results
'''
fig, ax = plt.subplots()
upper, median, lower = [], [], []
for i, data in enumerate(lengths):
up, med, low = np.percentile(data, [90, 50, 10])
upper.append(up)
median.append(med)
lower.append(low)
def step(n, loop_n, save_path, plot=True):
ds.simulate(n)
if plot:
ds.plot_neurons(
show_nodes=True, save_path=save_path)
