class TestMorhologySetsRotations(unittest.TestCase):
"""
Test scaffold with cells associated to a certain rotated morphology
"""
@classmethod
def setUpClass(self):
import dbbs_models
test_setup.prep_morphologies()
test_setup.prep_rotations()
super().setUpClass()
config = JSONConfig(config_file)
self.scaffold = Scaffold(config)
self.scaffold.morphology_repository = MorphologyRepository(test_setup.mr_rot_path)
def test_morphology_map(self):
# Create and place a set of 10 Golgi cells and assign them to a morphology based on their rotation
cell_type = self.scaffold.get_cell_type("golgi_cell")
positions = np.random.rand(9, 3)
# Construct rotation matrix for cell_type
phi_values = np.linspace(0.0, 360.0, num=3)
theta_values = np.linspace(0.0, 360.0, num=3)
phi_values = np.repeat(
phi_values, 3
) # np.random.choice(len(phi_values), len(positions))
theta_values = np.repeat(
theta_values, 3
) # np.random.choice(len(theta_values), len(positions))
rotations = np.vstack((phi_values, theta_values)).T
# Place cells and generate hdf5 output
self.scaffold.place_cells(
cell_type, cell_type.placement.layer_instance, positions, rotations
)
self.scaffold.compile_output()
ps = PlacementSet(self.scaffold.output_formatter, cell_type)
ms = MorphologySet(self.scaffold, cell_type, ps)
self.assertEqual(
len(rotations),
len(ms._morphology_index),
"Not all cells assigned to a morphology!",
)
random_sel = np.random.choice(len(ms._morphology_index))
morpho_sel = ms._morphology_map[ms._morphology_index[random_sel]]
self.assertTrue(
morpho_sel.find(
"__"
+ str(int(rotations[random_sel, 0]))
+ "_"
+ str(int(rotations[random_sel, 1]))
)
!= -1,
"Wrong morphology map!",
)
def test_spoofing(self):
"""
Assert that fake detailed connections can be made
"""
config = JSONConfig(file=_config)
scaffold = Scaffold(config)
scaffold.compile_network()
original_connections = len(
scaffold.cell_connections_by_tag["connection"])
sd = SpoofDetails()
sd.presynaptic = "from_cell"
sd.postsynaptic = "to_cell"
sd.scaffold = scaffold
# Raise error because here's no morphologies registered for the cell types.
with self.assertRaises(
MorphologyDataError,
msg="Missing morphologies during spoofing not caught."):
sd.after_connectivity()
# Add some morphologies
setattr(
config.cell_types["from_cell"].morphology,
"detailed_morphologies",
{"names": ["GranuleCell"]},
)
setattr(
config.cell_types["to_cell"].morphology,
"detailed_morphologies",
{"names": ["GranuleCell"]},
)
# Run the spoofing again
sd.after_connectivity()
cs = scaffold.get_connectivity_set("connection")
scaffold.compile_output()
# Use the intersection property. It throws an error should the detailed
# information be missing
try:
i = cs.intersections
for inter in i:
fl = inter.from_compartment.labels
tl = inter.to_compartment.labels
self.assertIn("axon", fl,
"From compartment type is not an axon")
self.assertIn("dendrites", tl,
"From compartment type is not a dendrite")
self.assertNotEqual(len(i), 0, "Empty intersection data")
self.assertEqual(len(i), original_connections,
"Different amount of spoofed connections")
except MissingMorphologyError:
self.fail("Could not find the intersection data on spoofed set")
# Set both types to relays and try spoofing again
config.cell_types["from_cell"].relay = True
config.cell_types["to_cell"].relay = True
with self.assertRaises(MorphologyError,
msg="Did not catch double relay spoofing!"):
sd.after_connectivity()
def test_aa_goc(self):
# 5) GrC (aa) - GoC
# To check it, 20syn on basal dendrites, not near the soma.
# AMPA/NMDA syn with a burst of 5 spike at 100Hz. The response should be a burst
# composed by 3 spikes
config = JSONConfig(aa_goc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_golgi"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("GolgiCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = m_gol.get_compartments(["basal_dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 20)
morpho_map = ["GranuleCell", "GolgiCell"]
morphologies = np.array([[0, 1]] * 20)
compartments = np.ones(
(20, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=20)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_grc_sc(self):
# 9) GrC - SC
# 3 random synapses on the dendrites. AMPA/NMDA, 10 spikes at 100Hz.
# It should do a burst of 5 spikes.
config = JSONConfig(grc_sc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_stellate"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("stellate_cell").identifiers
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
m_gol = scaffold.morphology_repository.get_morphology("StellateCell")
comps = m_gol.get_compartments(["dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 3)
morpho_map = ["GranuleCell", "StellateCell"]
morphologies = np.array([[0, 1]] * 3)
compartments = np.ones(
(3, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=3)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_pf_pc(self):
# 6) GrC (aa) - PC
# 100 random syn, on the apical dendrites. AMPA only, 10 spikes
# 500Hz. The response should be a burst composed by 3 spikes.
config = JSONConfig(aa_pc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_purkinje"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("purkinje_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("PurkinjeCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = [c.id for c in m_gol.compartments if c.type == 3]
conns = np.array([[grcs[0], golgis[0]]] * 80)
morpho_map = ["GranuleCell", "PurkinjeCell"]
morphologies = np.array([[0, 1]] * 80)
compartments = np.ones(
(80, 2)) * m_grc.get_compartments(["parallel_fiber"])[0].id
compartments[:, 1] = np.random.choice(comps, size=80)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_pf_goc(self):
# 7) GrC (pf) - GoC
# The same as 5) except on 80 apical dendrites.
config = JSONConfig(aa_goc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_golgi"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("GolgiCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = m_gol.get_compartments(["apical_dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 80)
morpho_map = ["GranuleCell", "GolgiCell"]
morphologies = np.array([[0, 1]] * 80)
compartments = np.ones(
(80, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=80)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_mf_granule(self):
config = JSONConfig(mf_grc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
mf_to_glom = scaffold.configuration.connection_types[
"mossy_to_glomerulus"]
glom_to_grc = scaffold.configuration.connection_types[
"glomerulus_to_granule"]
mfs = scaffold.get_placement_set("mossy_fibers").identifiers
gloms = scaffold.get_placement_set("glomerulus").identifiers
grcs = scaffold.get_placement_set("granule_cell").identifiers
scaffold.connect_cells(
mf_to_glom, np.array([[mfs[0], gloms[0]], [mfs[1], gloms[1]]]))
scaffold.connect_cells(
mf_to_glom, np.array([[mfs[0], gloms[0]], [mfs[1], gloms[1]]]))
conns = np.array([[gloms[0], grcs[0]], [gloms[1], grcs[0]]])
morpho_map = ["GranuleCell"]
morphologies = np.array([[0, 0], [0, 0]])
compartments = np.array([[0, 9], [0, 18]])
scaffold.connect_cells(
glom_to_grc,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure(
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
)).show()
def test_sc_pc(self):
# 9) GrC - SC
# 3 random synapses on the dendrites. AMPA/NMDA, 10 spikes at 100Hz.
# It should do a burst of 5 spikes.
config = JSONConfig(sc_pc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
]).show()
def test_glom_golgi_granule(self):
config = JSONConfig(mf_gol_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
mf_to_glom = scaffold.configuration.connection_types[
"mossy_to_glomerulus"]
glom_to_gc = scaffold.configuration.connection_types[
"glomerulus_to_golgi"]
gc_to_grc = scaffold.configuration.connection_types["golgi_to_granule"]
mfs = scaffold.get_placement_set("mossy_fibers").identifiers
gloms = scaffold.get_placement_set("glomerulus").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
granules = scaffold.get_placement_set("granule_cell").identifiers
scaffold.connect_cells(mf_to_glom, np.array([[mfs[0], gloms[0]]]))
conns = np.array([[gloms[0], golgis[0]]] * 20)
m = scaffold.morphology_repository.get_morphology("GolgiCell")
morpho_map = ["GolgiCell"]
morphologies = np.zeros((20, 2))
compartments = np.zeros((20, 2))
compartments[:, 1] = np.random.choice(
[c.id for c in m.compartments if c.type == 302], size=20)
scaffold.connect_cells(
glom_to_gc,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
conns_grc = np.array([[golgis[0], granules[0]]] * 4)
morpho_map_grc = ["GranuleCell", "GolgiCell"]
morphologies_grc = np.zeros((4, 2))
morphologies_grc[:, 0] = [1] * 4
compartments_grc = np.zeros((4, 2))
compartments_grc[:, 0] = [c.id for c in m.compartments
if c.type == 2][0:4]
compartments_grc[:, 1] = [9 * (i + 1) for i in range(4)]
scaffold.connect_cells(
gc_to_grc,
conns_grc,
morphologies=morphologies_grc,
compartments=compartments_grc,
morpho_map=morpho_map_grc,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
g = f["recorders/soma_voltages"]
a = f["recorders/axons"]
def L(g, s):
h = g[s]
return {"x": h[:, 0], "y": h[:, 1], "name": h.attrs["label"]}
go.Figure([
*(go.Scatter(**L(g, i)) for i in g),
*(go.Scatter(**L(a, i)) for i in a),
]).show()