# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with DeNSE. If not, see <http://www.gnu.org/licenses/>.
import matplotlib.pyplot as plt
import nngt
import dense as ds
from dense.units import *
from dense.elements import Population
pop = Population.from_swc(ds.NeuronsFromSimulation("2culture_swc"))
graph, intersections = ds.generate_network(pop)
for key in intersections:
intersections[key] = list(set(intersections[key]))
### Plot the graph in 2 subplots:
fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
nngt.plot.draw_network(graph, spatial=True, axis=ax3)
for neuron in pop:
if neuron < 100:
try:
ax1.plot(neuron.axon.xy[:, 0], neuron.axon.xy[:, 1], c='b')
except:
pass
else:
try:
# DeNSE is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with DeNSE. If not, see <http://www.gnu.org/licenses/>.
import matplotlib.pyplot as plt
import dense as ds
from dense.elements import Population
pop = Population.from_swc(ds.NeuronsFromSimulation("2culture_swc"))
graph, intersections, synapses = ds.generate_network(pop)
### Plot the graph in 2 subplots:
fig, (ax1, ax2) = plt.subplots(2, 1)
# nngt.plot.draw_network(graph,spatial = True,
# nsize="out-degree",
# ncolor="betweenness",
# esize=0.01,
# decimate =2,
# axis = ax2,
# dpi = 400)
ax1.set_title("Soma position")
for neuron in pop:
if neuron >= 100:
ax1.scatter(neuron.position[0], neuron.position[1], c='b')
else:
import sys
import matplotlib.pyplot as plt
import dense as ds
from dense.elements import Population
try:
culture_folder = sys.argv[1]
except:
raise ("add culture folder as first argument")
pop = Population.from_swc(ds.NeuronsFromSimulation(culture_folder))
graph, intersections, synapses = ds.generate_network(
pop, intersection_positions=True)
### Plot the graph in 2 subplots:
fig, (ax1, ax2) = plt.subplots(2, 1)
ax2.set_title("Connections as a directed graph")
# nngt.plot.draw_network(graph,spatial = True,
# nsize="out-degree",
# ncolor="betweenness",
# esize=0.01,
# decimate =2,
# axis = ax2,
# dpi = 400)
ax1.set_title("Positions of nurons' soma")
for neuron in pop:
ax1.scatter(neuron.position[0], neuron.position[1], c='r')
dendrites_params=dendrite_params,
axon_params=axon_params)
fig, ax = plt.subplots()
# ds.plot.plot_neurons(gid=range(100), culture=culture, soma_alpha=0.8,
# axon_color='g', gc_color="r", axis=ax, show=False)
# ds.plot.plot_neurons(gid=range(100, 200), show_culture=False, axis=ax,
# soma_alpha=0.8, axon_color='darkorange', gc_color="r",
# show=True)
# step(4000, 0, False)
# ~ for loop_n in range(5):
# ~ step(500, loop_n, True)
duration = time.time() - start
# prepare the plot
ds.plot.plot_neurons(gid=range(200), culture=culture, soma_alpha=0.8,
axon_color='g', gc_color="r", axis=ax, show=False)
ds.plot.plot_neurons(gid=range(200, 400), show_culture=False, axis=ax, ymax=500,
soma_alpha=0.8, axon_color='darkorange', gc_color="r",
show=True)
ds.plot.plot_neurons(gid=range(200, 400), show_culture=False, axis=ax,
soma_alpha=0.8, axon_color='yellow', gc_color="r",
show=True)
plt.show(block=True)
print("SIMULATION ENDED")
# save
graph =ds.generate_network()
save_path = CleanFolder(os.path.join(os.getcwd(),"diode_double_swc"))
ds.save_json_info(filepath=save_path)
ds.SaveSwc(filepath=save_path,swc_resolution = 10)
num_neurites=3)
start = time.time()
for i in range(10):
step(500, 0, True)
duration = time.time() - start
plt.show(block=True)
print("SIMULATION ENDED")
# save
save_path = CleanFolder(os.path.join(os.getcwd(), "swc"))
ds.save_json_info(filepath=save_path)
ds.SaveSwc(filepath=save_path, swc_resolution=10)
structure = ds.morphology.NeuronStructure()
graph = ds.generate_network(structure=structure)
# ds.reset_kernel()
### Import population for network analysis
# ng_population = ds.SimulationsFromFolder(save_path)
# import pdb; pdb.set_trace() # XXX BREAKPOINT
# population = ds.SwcEnsemble.from_population(ng_population)
# intersection = ds.IntersectionsFromEnsemble(population)
# num_connections = np.sum([len(a) for a in intersection.values()])
# graph = ds.generate_network(population, intersection)
# #graph info
# nngt.plot.degree_distribution(graph, ['in', 'out', 'total'])
# nngt.plot.draw_network(graph, esize=0.1, show=True)
import matplotlib.pyplot as plt
import dense as ds
from dense.elements import Population
try:
culture_folder = sys.argv[1]
except:
raise("add culture folder as first argument")
pop = ds.structure.Population.from_swc_population\
(ds.NeuronsFromSimulation(culture_folder))
graph, intersections, synapses = ds.generate_network(pop,\
intersection_positions=True,
do_graph=False)
### Plot the graph in 2 subplots:
fig, (ax1,ax2) = plt.subplots(2,1)
ax2.set_title("Connections as a directed graph")
# nngt.plot.draw_network(graph,spatial = True,
# nsize="out-degree",
# ncolor="betweenness",
# esize=0.01,
# decimate =2,
# axis = ax2,
# dpi = 400)
ax1.set_title("Positions of nurons' soma")