def setUpClass(self):
super(TestMultiInstance, self).setUpClass()
import nest
self.nest = nest
config = JSONConfig(file=single_neuron_config)
self.scaffold = Scaffold(config)
self.scaffold.compile_network()
self.hdf5 = self.scaffold.output_formatter.file
self.nest_adapter_0 = self.scaffold.get_simulation(
"test_single_neuron")
# When another test errors, the lock might remain, and all locking tests fail
self.nest_adapter_0.delete_lock()
self.nest_adapter_1 = self.scaffold.create_adapter(
"test_single_neuron")
self.nest_adapter_2 = self.scaffold.create_adapter(
"test_single_neuron")
self.nest_adapter_multi_1 = self.scaffold.create_adapter(
"test_single_neuron")
self.nest_adapter_multi_1.enable_multi("first")
self.nest_adapter_multi_1b = self.scaffold.create_adapter(
"test_single_neuron")
self.nest_adapter_multi_1b.enable_multi("first")
self.nest_adapter_multi_2 = self.scaffold.create_adapter(
"test_single_neuron")
self.nest_adapter_multi_2.enable_multi("second")
def setUpClass(self):
super().setUpClass()
import nest
self.nest = nest
config = JSONConfig(file=recorder_config)
self.scaffold = Scaffold(config)
def setUpClass(cls):
super().setUpClass()
config = JSONConfig(file=heterosyn_config)
cls.scaffold = Scaffold(config)
cls.scaffold.compile_network()
cls.nest_adapter = cls.scaffold.run_simulation(
"test_double_neuron_network_heterosyn")
def setUpClass(self):
super(TestFiberIntersection, self).setUpClass()
# Make sure the MR exists
test_setup.prep_morphologies()
# The scaffold has only the Granular layer (100x100x150) with 20 GrCs
# and 1 GoC placed, as specified in the config file
self.config = JSONConfig(file=fiber_transform_config)
# Defining quivers field to include also voxels outside the scaffold
# volume
self.quivers_field = np.zeros(
shape=(3, 80, 80, 80)) # Each voxel in the volume has vol_res=25um
# Fake quiver, oriented as original fibers
basic_quiver = np.array([0, 1.0, 0.0])
self.quivers_field[0, :] = basic_quiver[0]
self.quivers_field[1, :] = basic_quiver[1]
self.quivers_field[2, :] = basic_quiver[2]
self.config.connection_types[
"parallel_fiber_to_golgi_bended"].transformation.quivers = self.quivers_field
self.config.connection_types[
"parallel_fiber_to_golgi_bended"].transformation.vol_start = [
-500.0, -500.0, -500.0
]
self.scaffold = Scaffold(self.config)
self.scaffold.morphology_repository = MorphologyRepository(morpho_file)
self.scaffold.compile_network()
def setUp(self):
config = JSONConfig(file=single_neuron_config)
self.scaffold = Scaffold(config)
self.scaffold.compile_network()
self.nest_adapter = self.scaffold.configuration.simulations[
"test_single_neuron"]
self.nest_adapter.reset()
def test_cortex_model_synapses(self):
cfg = JSONConfig(config)
_ = Scaffold(cfg)
for name, model in cfg.simulations["poc"].cell_models.items():
if model.relay:
continue
with self.subTest(model=name):
self._test_model(model.model_class)
def setUpClass(cls):
super().setUpClass()
config = JSONConfig(file=double_nn_config)
if not neuron_installed():
del config.simulations["neuron"]
cls.scaffold = Scaffold(config)
cls.scaffold.compile_network()
cls.nest_adapter = cls.scaffold.run_simulation(
"test_double_neuron_network_static")
def test_teaching_connection_missing(self):
from bsb.exceptions import ConfigurationError
with open(heterosyn_config, "r") as f:
stream = f.read()
stream = stream.replace('"teaching": "teaching_cell_to_cell",', "")
with self.assertRaises(ConfigurationError) as ce:
config = JSONConfig(stream=stream)
self.scaffold = Scaffold(config)
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_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 setUpClass(self):
import nest
nest.ResetKernel()
super(TestEntities, self).setUpClass()
config = JSONConfig(file=minimal_config_entities)
self.scaffold = Scaffold(config)
self.scaffold.compile_network()
hdf_config = _from_hdf5("minimal_entities.hdf5")
self.scaffold_fresh = Scaffold(hdf_config,
from_file="minimal_entities.hdf5")
def test_215(self):
"""
Assert that reconfiguring an HDF5 doesn't exist doesn't create a gimpy empty
HDF5 file that causes a downstream error.
"""
config = JSONConfig(file="mouse_cerebellum_cortex")
self.assertRaises(
FileNotFoundError,
HDF5Formatter.reconfigure,
"thisHDF5_doesntexist.hdf",
config,
)
def setUpClass(self):
import dbbs_models
test_setup.prep_morphologies()
test_setup.prep_rotations()
super().setUpClass()
config = JSONConfig(config_file)
self.scaffold = Scaffold(config)
mr = MorphologyRepository(test_setup.mr_rot_path)
self.scaffold.morphology_repository = mr
self.morphology_cache = MorphologyCache(mr)
self.morphologies_start = ["GranuleCell", "GolgiCell", "GolgiCell_A"]
self.morphologies_rotated = mr.list_morphologies(include_rotations=True)
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_representatives(self):
"""
Test that 1 cell per non-relay cell model is chosen.
"""
from patch import p
config = JSONConfig(double_nn_config)
scaffold = Scaffold(config)
scaffold.compile_network()
adapter = scaffold.create_adapter("neuron")
adapter.h = p
adapter.load_balance()
device = adapter.devices["test_representatives"]
device.initialise_targets()
targets = adapter.devices["test_representatives"].get_targets()
self.assertEqual(
1,
len(targets),
"Targetting type `representatives` did not return the correct amount of representatives.",
)
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()
import cProfile
import numpy as np
import os, sys, pstats
from time import sleep
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from bsb import Scaffold
from bsb.config import JSONConfig
config = JSONConfig("../test.json")
instance = Scaffold(config)
for i in range(1, 40):
config.resize(100 + i * 20, 100 + i * 20)
instance.reset_network_cache()
cProfile.run("instance.compile_network()", "compile_stats")
p = pstats.Stats("compile_stats")
p.strip_dirs().sort_stats("cumulative").print_stats("place", 25)
print("square size:", config.X)
sleep(1)
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()
# Example that shows how to overwrite the configuration inside of an existing network
from bsb.output import HDF5Formatter
from bsb.config import JSONConfig
config = JSONConfig("new_config.json")
HDF5Formatter.reconfigure("my_network.hdf5", config)
def setUpClass(self):
super(TestPlacementSets, self).setUpClass()
test_setup.prep_morphologies()
config = JSONConfig(file=double_neuron_config)
self.scaffold = Scaffold(config)
self.scaffold.compile_network()
def test_minimal(self):
config = JSONConfig(file=minimal_config)
self.scaffold = Scaffold(config)
def setUpClass(self):
super(TestSingleTypeCompilation, self).setUpClass()
config = JSONConfig(file=single_neuron_config)
self.scaffold = Scaffold(config)
self.scaffold.compile_network()