def test_functions():
'''
Run each of the main functions.
'''
ds.reset_kernel()
ds.set_kernel_status('environment_required', False)
m = ds.generate_model('constant', 'memory-based', 'run-and-tumble')
dp = ds.get_default_properties(m)
e = ds.get_environment()
ks = ds.get_kernel_status()
ms = ds.get_models()
pp = {"growth_cone_model": m, "position": (0., 0.) * um}
gn = ds.create_neurons(params=pp, num_neurites=2)
n = ds.get_neurons()
ns = ds.get_object_properties(n)
ns = ds.get_object_properties(n, level="neurite")
ns = ds.get_object_properties(n, level="growth_cone")
ns = ds.get_object_state(n)
assert ds.get_object_state(n, "num_growth_cones") == 2
ns = ds.get_object_state(n, level="dendrite_1")
si = ds.get_simulation_id()
ds.simulate(20 * hour)
ni = ds.get_neurons()
def run_dense(neuron_params, axes, letter, single=False):
# neuron_params["rw_memory_tau"]: 4.,
# neuron_params["rw_delta_corr"]: 1.8,
ds.set_kernel_status(kernel, simulation_id="random_walk_axons")
neuron_params["position"] = np.zeros((num_neurons, 2)) * um
simulated_neurons = num_neurons
if single:
simulated_neurons = 1.
neuron_params["position"] = np.array([0, 0]) * um
gids = ds.create_neurons(n=simulated_neurons,
params=neuron_params,
num_neurites=1,
position=[])
step(1000 * second, 1, os.path.join(os.getcwd(), "primo"), plot=False)
neurons = ds.get_neurons()
structure = ds.morphology.NeuronStructure(neurons)
population = ds.Population.from_structure(structure)
axons = population.axon_all_points()
from matplotlib.colors import LogNorm
import matplotlib
import copy
axes.text(0.03,
1.2,
letter,
horizontalalignment='center',
verticalalignment='center',
weight='bold',
fontsize=12,
transform=axes.transAxes)
if not single:
my_cmap = copy.copy(
matplotlib.cm.get_cmap('viridis')) # copy the default cmap
my_cmap.set_bad((0, 0, 0))
axes.hist2d(axons[:, 0],
axons[:, 1],
bins=100,
range=[[0, 400], [-200, 200]],
norm=LogNorm(),
cmap=my_cmap)
axes.set_xlabel("X")
axes.set_ylabel("Y")
axes.set_title("path density for\n {}".format(
neuron_params["growth_cone_model"]))
else:
axes.plot(axons[:, 0], axons[:, 1], c='r')
ds.reset_kernel()
def run_dense(neuron_params,letter,length_simulation,single=False):
# neuron_params["rw_memory_tau"]: 4.,
# neuron_params["rw_delta_corr"]: 1.8,
ds.set_kernel_status(kernel, simulation_id="random_walk_axons")
neuron_params["position"]=np.zeros((num_neurons,2))
simulated_neurons = num_neurons
gids = ds.create_neurons(n=simulated_neurons,
params=neuron_params,
num_neurites=1,
position=[]
)
step(length_simulation, 1, False ,plot=False)
neurons = ds.get_neurons()
structure = ds.morphology.NeuronStructure(neurons)
population = ds.Population.from_structure(structure)
ens = ds.EnsembleRW(population)
ens.characterizeRW("axon")
ens.name = neuron_params["growth_cone_model"]
fits = ens.fit()
# import pdb; pdb.set_trace() # XXX BREAKPOINT
ds.reset_kernel()
return ens
# 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()
# ~ asym.append(np.average(neurom.fst.get("partition_asymmetry", nrn)))
def test_delete_neurons():
'''
Neurons deletion
'''
ds.reset_kernel()
num_neurons = 50
simtime = 2.*minute
initial_state = np.random.get_state()
ds.set_kernel_status("environment_required", False)
# create and delete
nparams = {
"position": np.random.uniform(-1000, 1000, (num_neurons, 2))*um,
"growth_cone_model": "res_po_rt"
}
neurons = ds.create_neurons(num_neurons, params=nparams,
num_neurites=2)
ds.delete_neurons(3)
ds.delete_neurons(neurons[8])
# check neurons have been deleted
neurons_got = ds.get_neurons()
n_to_ints = [int(n) for n in neurons_got]
n_to_ints += ds.get_neurons(True)
assert len(n_to_ints) == 2*(num_neurons - 2), \
"Failed with state " + str(initial_state)
assert 3 not in n_to_ints, \
"Failed with state " + str(initial_state)
assert 8 not in n_to_ints, \
"Failed with state " + str(initial_state)
# simulate then delete
ds.simulate(simtime)
ds.delete_neurons([5, 7])
ds.delete_neurons(neurons[40:45])
# recreate neurons and resimulate
nparams = {
"position": np.random.uniform(-1000, 1000, (num_neurons, 2))*um,
"growth_cone_model": "res_po_rt"
}
_ = ds.create_neurons(num_neurons, params=nparams, num_neurites=2)
ds.simulate(simtime)
neurons_got = ds.get_neurons()
n_to_ints = [int(n) for n in neurons_got]
n_to_ints += ds.get_neurons(True)
assert len(n_to_ints) == 2*(2*num_neurons - 4 - len(neurons[40:45])), \
"Failed with state " + str(initial_state)
assert 5 not in n_to_ints, \
"Failed with state " + str(initial_state)
assert 7 not in n_to_ints, \
"Failed with state " + str(initial_state)
for n in neurons[40:45]:
assert int(n) not in n_to_ints, \
"Failed with state " + str(initial_state)
# delete all neurons
ds.delete_neurons()
# check no neurons are left
assert not ds.get_neurons(), \
"Failed with state " + str(initial_state)