# 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 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
# You should have received a copy of the GNU General Public License
# along with DeNSE. If not, see <http://www.gnu.org/licenses/>.
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)
# 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.units import *
from dense.elements import Population
pop = Population.from_swc(ds.NeuronsFromSimulation("circular_swc"))
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")
graph.to_file("circular.el")
# nngt.plot.draw_network(graph,spatial = True,
# nsize="out-degree",
# ncolor="betweenness",
# esize=0.01,
# decimate =2,
# axis = ax2,
# dpi = 400)
# 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.units import *
from dense.elements import Population
# pop = Population.from_swc(ds.NeuronsFromSimulation("circular_swc"))
pop = Population.from_swc(ds.io.load_swc("circular_swc"))
graph, intersections, synapses = ds.morphology.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")
graph.to_file("circular.el")
# nngt.plot.draw_network(graph,spatial = True,
# nsize="out-degree",
# ncolor="betweenness",
# esize=0.01,
# decimate =2,
# axis = ax2,
# dpi = 400)
ha='left',
va='top')
dx.text(np.radians(-30),
dx.get_rmax(),
label,
rotation=0.,
ha='left',
va='top')
plt.tight_layout()
plt.show()
# plt.savefig("sholl_analysis.png", format='png',ppi=300)
# axes.scatter(0,0,c='k')
if __name__ == "__main__":
pop = Population(sys.argv[1])
import argparse
parser = argparse.ArgumentParser(description='Sholl analysis')
parser.add_argument('--culture', type=str, help='folder with swc files')
parser.add_argument('--no_import',
action='store_false',
default=True,
help='do not import the population from file')
args = parser.parse_args()
if args.no_import:
for n in range(6):
swc_culture = ds.NeuronsFromSimulation(args.culture + str(n))
pop.info = swc_culture["info"]
pop.add_swc_population(swc_culture['neurons'])
# (at your option) any later version.
#
# 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("2chambers_test.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: