def setUp(self):
os.chdir(os.path.dirname(__file__))
self.network_path = os.path.join("networks", "network_testing_input")
self.config_file = os.path.join(self.network_path,
"network-config.json")
self.position_file = os.path.join(self.network_path,
"network-neuron-positions.hdf5")
self.save_file = os.path.join(self.network_path, "voxels",
"network-putative-synapses.hdf5")
# Setup network so we can test input generation
from snudda.init.init import SnuddaInit
cell_spec = os.path.join(os.path.dirname(__file__), "validation")
cnc = SnuddaInit(struct_def={},
config_file=self.config_file,
random_seed=1234)
cnc.define_striatum(num_dSPN=5,
num_iSPN=0,
num_FS=5,
num_LTS=0,
num_ChIN=0,
volume_type="cube",
neurons_dir=cell_spec)
cnc.write_json(self.config_file)
# Place neurons
from snudda.place.place import SnuddaPlace
npn = SnuddaPlace(
config_file=self.config_file,
log_file=None,
verbose=True,
d_view=
None, # TODO: If d_view is None code run sin serial, add test parallel
h5libver="latest")
npn.parse_config()
npn.write_data(self.position_file)
# Detect
self.sd = SnuddaDetect(config_file=self.config_file,
position_file=self.position_file,
save_file=self.save_file,
rc=None,
hyper_voxel_size=120,
verbose=True)
self.sd.detect(restart_detection_flag=True)
# Prune
self.network_file = os.path.join(self.network_path,
"network-synapses.hdf5")
sp = SnuddaPrune(network_path=self.network_path,
config_file=None) # Use default config file
sp.prune(pre_merge_only=False)
def setup_network(self, neurons_path, num_replicas=10, neuron_types=None):
# TODO: num_replicas should be set by a parameter, it affects how many duplicates of each neuron
# and thus how many steps we have between n_min and n_max number of inputs specified.
config_def = self.create_network_config(neurons_path=neurons_path,
num_replicas=num_replicas,
neuron_types=neuron_types)
print(
f"Writing network config file to {self.network_config_file_name}")
with open(self.network_config_file_name, "w") as f:
json.dump(config_def, f, indent=2, cls=NumpyEncoder)
create_cube_mesh(os.path.join("data", "mesh", "InputTestMesh.obj"),
[0, 0, 0],
1e-3,
description="Mesh file used for Input Scaling")
# Write the neurons path to file
self.write_tuning_info()
from snudda.place.place import SnuddaPlace
from snudda.detect.detect import SnuddaDetect
from snudda.detect.prune import SnuddaPrune
sp = SnuddaPlace(network_path=self.network_path)
sp.parse_config()
sp.write_data()
sd = SnuddaDetect(network_path=self.network_path)
sd.detect()
sp = SnuddaPrune(network_path=self.network_path)
sp.prune()
def prune_synapses(self, args):
# self.networkPath = args.path
print("Prune synapses")
print("Network path: " + str(self.network_path))
from snudda.detect.prune import SnuddaPrune
log_filename = os.path.join(self.network_path, "log",
"logFile-synapse-pruning.txt")
random_seed = args.randomseed
self.setup_log_file(log_filename) # sets self.logfile
if args.parallel:
self.setup_parallel() # sets self.d_view and self.lb_view
# Optionally set this
scratch_path = None
if args.merge_only:
pre_merge_only = True
else:
pre_merge_only = False
print(f"preMergeOnly : {pre_merge_only}")
if args.h5legacy:
h5libver = "earliest"
else:
h5libver = "latest" # default
sp = SnuddaPrune(network_path=self.network_path,
logfile=self.logfile,
logfile_name=log_filename,
config_file=args.config_file,
d_view=self.d_view,
lb_view=self.lb_view,
scratch_path=scratch_path,
h5libver=h5libver,
random_seed=random_seed,
verbose=args.verbose)
sp.prune(pre_merge_only=pre_merge_only)
self.stop_parallel()
self.close_log_file()
def prune_network(self, pruning_config=None, fig_name=None, title=None, verbose=False, plot_network=True,
random_seed=None, n_repeats=None):
if n_repeats is None:
n_repeats = self.n_repeats
work_log = os.path.join(self.network_path, "log", "network-detect-worklog.hdf5")
pruned_output = os.path.join(self.network_path, "network-synapses.hdf5")
if pruning_config is not None and not os.path.exists(pruning_config):
pruning_config = os.path.join(self.network_path, pruning_config)
# We keep temp files
sp = SnuddaPrune(network_path=self.network_path, config_file=pruning_config,
verbose=verbose, keep_files=True, random_seed=random_seed) # Use default config file
sp.prune()
n_synapses = sp.out_file["network/synapses"].shape[0]
n_gap_junctions = sp.out_file["network/gapJunctions"].shape[0]
sp = []
plot_axon = True
plot_dendrite = True
#plot_axon = np.ones((20,), dtype=bool)
#plot_dendrite = np.ones((20,), dtype=bool)
#plot_axon[:10] = False
#plot_dendrite[10:] = False
if plot_network:
pn = PlotNetwork(pruned_output)
plt, ax = pn.plot(fig_name=fig_name, show_axis=False,
plot_axon=plot_axon, plot_dendrite=plot_dendrite,
title=title, title_pad=-14,
elev_azim=(90, 0))
if n_repeats > 1:
n_syn_mean, n_syn_std, _, _ = self.gather_pruning_statistics(pruning_config=pruning_config, n_repeats=n_repeats)
plt.figtext(0.5, 0.15, f"(${n_syn_mean:.1f} \pm {n_syn_std:.1f}$)", ha="center", fontsize=16)
plt.savefig(fig_name, dpi=300, bbox_inches='tight')
# Load the pruned data and check it
# sl = SnuddaLoad(pruned_output)
return n_synapses, n_gap_junctions
def prune_network(self, pruning_config=None, fig_name=None, title=None):
work_log = os.path.join(self.network_path, "log", "network-detect-worklog.hdf5")
pruned_output = os.path.join(self.network_path, "network-synapses.hdf5")
if pruning_config is not None and not os.path.exists(pruning_config):
pruning_config = os.path.join(self.network_path, pruning_config)
sp = SnuddaPrune(network_path=self.network_path, config_file=pruning_config) # Use default config file
sp.prune(pre_merge_only=False)
sp = []
plot_axon = True
plot_dendrite = True
#plot_axon = np.ones((20,), dtype=bool)
#plot_dendrite = np.ones((20,), dtype=bool)
#plot_axon[:10] = False
#plot_dendrite[10:] = False
pn = PlotNetwork(pruned_output)
plt, ax = pn.plot(fig_name=fig_name, show_axis=False,
plot_axon=plot_axon, plot_dendrite=plot_dendrite,
title=title, title_pad=-14,
elev_azim=(90, 0))
def __init__(self):
if os.path.dirname(__file__):
os.chdir(os.path.dirname(__file__))
self.network_path = "touch_detection_illustration_network"
self.config_file = os.path.join(self.network_path, "network-config.json")
self.position_file = os.path.join(self.network_path, "network-neuron-positions.hdf5")
self.save_file = os.path.join(self.network_path, "voxels", "network-putative-synapses.hdf5")
create_cube_mesh(file_name=os.path.join(self.network_path, "mesh", "simple_mesh.obj"),
centre_point=(0, 0, 0),
side_len=500e-6)
sp = SnuddaPlace(config_file=self.config_file, d_view=None)
sp.parse_config()
sp.write_data(self.position_file)
self.sd = SnuddaDetect(config_file=self.config_file, position_file=self.position_file,
save_file=self.save_file, rc=None,
hyper_voxel_size=150)
# Reposition the neurons so we know how many synapses and where they will be located before pruning
neuron_positions = np.array([[0, 59, 0], # Postsynaptiska
[0, 89, 0],
[0, 119, 0],
[0, 149, 0],
[0, 179, 0],
[0, 209, 0],
[0, 239, 0],
[0, 269, 0],
[0, 299, 0],
[0, 329, 0],
[59, 0, 0], # Presynaptiska
[89, 0, 0],
[119, 0, 0],
[149, 0, 0],
[179, 0, 0],
[209, 0, 0],
[239, 0, 0],
[269, 0, 0],
[299, 0, 0],
[329, 0, 0],
]) * 1e-6
for idx, pos in enumerate(neuron_positions):
self.sd.neurons[idx]["position"] = pos
ang = -np.pi / 2
R_x = np.array([[1, 0, 0],
[0, np.cos(ang), -np.sin(ang)],
[0, np.sin(ang), np.cos(ang)]])
ang = np.pi / 2
R_y = np.array([[np.cos(ang), 0, np.sin(ang)],
[0, 1, 0],
[-np.sin(ang), 0, np.cos(ang)]])
for idx in range(0, 10): # Post synaptic neurons
self.sd.neurons[idx]["rotation"] = R_x
for idx in range(10, 20): # Presynaptic neurons
self.sd.neurons[idx]["rotation"] = R_y
self.sd.detect(restart_detection_flag=True)
# Also update so that the new positions are saved in the place file
rn = RepositionNeurons(self.position_file)
for neuron_info in self.sd.neurons:
rn.place(neuron_info["neuronID"], position=neuron_info["position"], rotation=neuron_info["rotation"],
verbose=False)
rn.close()
sp = SnuddaPrune(network_path=self.network_path) # Use default config file
sp.prune()
sp = []
def setUp(self):
from snudda.place.create_cube_mesh import create_cube_mesh
# Create cube meshes
self.network_path = os.path.join("networks", "network_testing_project")
mesh_file_a = os.path.join(self.network_path, "mesh", "volume_A.obj")
mesh_file_b = os.path.join(self.network_path, "mesh", "volume_B.obj")
create_cube_mesh(mesh_file_a, [5e-3, 0, 0], 300e-6,
"Volume A - connect structures example")
create_cube_mesh(mesh_file_b, [-5e-3, 0, 0], 300e-6,
"Volume B - connect structures example")
# Define network
from snudda.init.init import SnuddaInit
cnc = SnuddaInit(network_path=self.network_path, random_seed=123)
cnc.define_structure(struct_name="VolumeA",
struct_mesh=mesh_file_a,
d_min=15e-6,
mesh_bin_width=50e-6)
cnc.define_structure(struct_name="VolumeB",
struct_mesh=mesh_file_b,
d_min=15e-6,
mesh_bin_width=50e-6)
cnc.add_neurons(name="dSPN",
num_neurons=20,
volume_id="VolumeA",
neuron_dir=os.path.join("$DATA", "neurons", "striatum",
"dspn"))
cnc.add_neurons(name="iSPN",
num_neurons=20,
volume_id="VolumeB",
neuron_dir=os.path.join("$DATA", "neurons", "striatum",
"ispn"))
# Add the projection we want to test dSPN->iSPN
proj_file = os.path.join("data", "ExampleProjection.json")
cnc.neuron_projection(neuron_name="dSPN",
target_name="iSPN",
projection_name="ExampleProjection",
projection_file=proj_file,
source_volume="VolumeA",
dest_volume="VolumeB",
projection_radius=100e-6,
number_of_targets=[10, 5],
number_of_synapses=[10, 5],
dendrite_synapse_density="1",
connection_type="GABA",
dist_pruning=None,
f1=0.9,
soft_max=None,
mu2=None,
a3=None)
# Also add dSPN-dSPN and iSPN-iSPN synapses
# Note we do NOT add dSPN-iSPN again this way, as that would overwrite the above connections
# (The above neuron_projection will also do normal touch detection)
SPN2SPNdistDepPruning = "1-exp(-(0.4*d/60e-6)**2)"
MSD1gGABA = [0.24e-9, 0.1e-9]
MSD2gGABA = [0.24e-9, 0.1e-9]
MSD1GABAfailRate = 0.7 # Taverna 2008, figure 2
MSD2GABAfailRate = 0.4 # Taverna 2008, 2mM
pfdSPNdSPN = os.path.join("$DATA", "synapses", "striatum",
"PlanertFitting-DD-tmgaba-fit.json")
pfdSPNiSPN = os.path.join("$DATA", "synapses", "striatum",
"PlanertFitting-DI-tmgaba-fit.json")
pfiSPNdSPN = os.path.join("$DATA", "synapses", "striatum",
"PlanertFitting-ID-tmgaba-fit.json")
pfiSPNiSPN = os.path.join("$DATA", "synapses", "striatum",
"PlanertFitting-II-tmgaba-fit.json")
cnc.add_neuron_target(neuron_name="dSPN",
target_name="dSPN",
connection_type="GABA",
dist_pruning=SPN2SPNdistDepPruning,
f1=0.38,
soft_max=3,
mu2=2.4,
a3=1.0,
conductance=MSD1gGABA,
parameter_file=pfdSPNdSPN,
mod_file="tmGabaA",
channel_param_dictionary={
"tau1": (1.3e-3, 1e3),
"tau2": (12.4e-3, 1e3),
"failRate": MSD1GABAfailRate
})
cnc.add_neuron_target(neuron_name="iSPN",
target_name="iSPN",
connection_type="GABA",
dist_pruning=SPN2SPNdistDepPruning,
f1=0.55,
soft_max=4,
mu2=2.4,
a3=1.0,
conductance=MSD2gGABA,
parameter_file=pfiSPNiSPN,
mod_file="tmGabaA",
channel_param_dictionary={
"tau1": (1.3e-3, 1e3),
"tau2": (12.4e-3, 1e3),
"failRate": MSD2GABAfailRate
})
cnc.write_json()
# Place neurons, then detect, project and prune
from snudda.place.place import SnuddaPlace
sp = SnuddaPlace(network_path=self.network_path, verbose=True)
sp.parse_config()
sp.write_data()
from snudda.detect.detect import SnuddaDetect
sd = SnuddaDetect(network_path=self.network_path,
hyper_voxel_size=100,
verbose=True)
sd.detect()
from snudda.detect.project import SnuddaProject
sp = SnuddaProject(network_path=self.network_path)
sp.project()
sp.write()
from snudda.detect.prune import SnuddaPrune
sp = SnuddaPrune(network_path=self.network_path, verbose=True)
sp.prune()
def test_project(self):
# Are there connections dSPN->iSPN
from snudda.utils.load import SnuddaLoad
network_file = os.path.join(self.network_path, "network-synapses.hdf5")
sl = SnuddaLoad(network_file)
dspn_id_list = sl.get_cell_id_of_type("dSPN")
ispn_id_list = sl.get_cell_id_of_type("iSPN")
tot_proj_ctr = 0
for dspn_id in dspn_id_list:
for ispn_id in ispn_id_list:
synapses, synapse_coords = sl.find_synapses(pre_id=dspn_id,
post_id=ispn_id)
if synapses is not None:
tot_proj_ctr += synapses.shape[0]
with self.subTest(stage="projection_exists"):
# There should be projection synapses between dSPN and iSPN in this toy example
self.assertTrue(tot_proj_ctr > 0)
tot_dd_syn_ctr = 0
for dspn_id in dspn_id_list:
for dspn_id2 in dspn_id_list:
synapses, synapse_coords = sl.find_synapses(pre_id=dspn_id,
post_id=dspn_id2)
if synapses is not None:
tot_dd_syn_ctr += synapses.shape[0]
tot_ii_syn_ctr = 0
for ispn_id in ispn_id_list:
for ispn_id2 in ispn_id_list:
synapses, synapse_coords = sl.find_synapses(pre_id=ispn_id,
post_id=ispn_id2)
if synapses is not None:
tot_ii_syn_ctr += synapses.shape[0]
with self.subTest(stage="normal_synapses_exist"):
# In this toy example neurons are quite sparsely placed, but we should have at least some
# synapses
self.assertTrue(tot_dd_syn_ctr > 0)
self.assertTrue(tot_ii_syn_ctr > 0)
# We need to run in parallel also to verify we get same result (same random seed)
serial_synapses = sl.data["synapses"].copy()
del sl # Close old file so we can overwrite it
os.environ["IPYTHONDIR"] = os.path.join(os.path.abspath(os.getcwd()),
".ipython")
os.environ["IPYTHON_PROFILE"] = "default"
os.system(
"ipcluster start -n 4 --profile=$IPYTHON_PROFILE --ip=127.0.0.1&")
time.sleep(10)
# Run place, detect and prune in parallel by passing rc
from ipyparallel import Client
u_file = os.path.join(".ipython", "profile_default", "security",
"ipcontroller-client.json")
rc = Client(url_file=u_file, timeout=120, debug=False)
d_view = rc.direct_view(
targets='all') # rc[:] # Direct view into clients
from snudda.detect.detect import SnuddaDetect
sd = SnuddaDetect(network_path=self.network_path,
hyper_voxel_size=100,
rc=rc,
verbose=True)
sd.detect()
from snudda.detect.project import SnuddaProject
# TODO: Currently SnuddaProject only runs in serial
sp = SnuddaProject(network_path=self.network_path)
sp.project()
sp.write()
from snudda.detect.prune import SnuddaPrune
# Prune has different methods for serial and parallel execution, important to test it!
sp = SnuddaPrune(network_path=self.network_path, rc=rc, verbose=True)
sp.prune()
with self.subTest(stage="check-parallel-identical"):
sl2 = SnuddaLoad(network_file)
parallel_synapses = sl2.data["synapses"].copy()
# ParameterID, sec_X etc are randomised in hyper voxel, so you need to use same
# hypervoxel size for reproducability between serial and parallel execution
# All synapses should be identical regardless of serial or parallel execution path
self.assertTrue((serial_synapses == parallel_synapses).all())
os.system("ipcluster stop")
def test_prune(self):
pruned_output = os.path.join(self.network_path,
"network-synapses.hdf5")
with self.subTest(stage="No-pruning"):
sp = SnuddaPrune(network_path=self.network_path,
config_file=None,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
sp = []
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# TODO: Call a plot function to plot entire network with synapses and all
self.assertEqual(sl.data["nSynapses"], (20 * 8 + 10 * 2) *
2) # Update, now AMPA+GABA, hence *2 at end
# This checks that all synapses are in order
# The synapse sort order is destID, sourceID, synapsetype (channel model id).
syn = sl.data["synapses"][:sl.data["nSynapses"], :]
syn_order = (syn[:, 1] * len(self.sd.neurons) + syn[:, 0]
) * 12 + syn[:, 6] # The 12 is maxChannelModelID
self.assertTrue((np.diff(syn_order) >= 0).all())
# Note that channel model id is dynamically allocated, starting from 10 (GJ have ID 3)
# Check that correct number of each type
self.assertEqual(np.sum(sl.data["synapses"][:, 6] == 10),
20 * 8 + 10 * 2)
self.assertEqual(np.sum(sl.data["synapses"][:, 6] == 11),
20 * 8 + 10 * 2)
self.assertEqual(sl.data["nGapJunctions"], 4 * 4 * 4)
gj = sl.data["gapJunctions"][:sl.data["nGapJunctions"], :2]
gj_order = gj[:, 1] * len(self.sd.neurons) + gj[:, 0]
self.assertTrue((np.diff(gj_order) >= 0).all())
with self.subTest(stage="load-testing"):
sl = SnuddaLoad(pruned_output, verbose=True)
# Try and load a neuron
n = sl.load_neuron(0)
self.assertTrue(type(n) == NeuronMorphology)
syn_ctr = 0
for s in sl.synapse_iterator(chunk_size=50):
syn_ctr += s.shape[0]
self.assertEqual(syn_ctr, sl.data["nSynapses"])
gj_ctr = 0
for gj in sl.gap_junction_iterator(chunk_size=50):
gj_ctr += gj.shape[0]
self.assertEqual(gj_ctr, sl.data["nGapJunctions"])
syn, syn_coords = sl.find_synapses(pre_id=14)
self.assertTrue((syn[:, 0] == 14).all())
self.assertEqual(syn.shape[0], 40)
syn, syn_coords = sl.find_synapses(post_id=3)
self.assertTrue((syn[:, 1] == 3).all())
self.assertEqual(syn.shape[0], 36)
cell_id_perm = sl.get_cell_id_of_type("ballanddoublestick",
random_permute=True,
num_neurons=28)
cell_id = sl.get_cell_id_of_type("ballanddoublestick",
random_permute=False)
self.assertEqual(len(cell_id_perm), 28)
self.assertEqual(len(cell_id), 28)
for cid in cell_id_perm:
self.assertTrue(cid in cell_id)
# It is important merge file has synapses sorted with dest_id, source_id as sort order since during pruning
# we assume this to be able to quickly find all synapses on post synaptic cell.
# TODO: Also include the ChannelModelID in sorting check
with self.subTest("Checking-merge-file-sorted"):
for mf in [
"temp/synapses-for-neurons-0-to-28-MERGE-ME.hdf5",
"temp/gapJunctions-for-neurons-0-to-28-MERGE-ME.hdf5",
"network-synapses.hdf5"
]:
merge_file = os.path.join(self.network_path, mf)
sl = SnuddaLoad(merge_file, verbose=True)
if "synapses" in sl.data:
syn = sl.data["synapses"][:sl.data["nSynapses"], :2]
syn_order = syn[:, 1] * len(self.sd.neurons) + syn[:, 0]
self.assertTrue((np.diff(syn_order) >= 0).all())
if "gapJunctions" in sl.data:
gj = sl.data["gapJunctions"][:sl.data["nGapJunctions"], :2]
gj_order = gj[:, 1] * len(self.sd.neurons) + gj[:, 0]
self.assertTrue((np.diff(gj_order) >= 0).all())
with self.subTest("synapse-f1"):
# Test of f1
testing_config_file = os.path.join(self.network_path,
"network-config-test-1.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output, verbose=True)
# Setting f1=0.5 in config should remove 50% of GABA synapses, but does so randomly, for AMPA we used f1=0.9
gaba_id = sl.data["connectivityDistributions"][
"ballanddoublestick",
"ballanddoublestick"]["GABA"]["channelModelID"]
ampa_id = sl.data["connectivityDistributions"][
"ballanddoublestick",
"ballanddoublestick"]["AMPA"]["channelModelID"]
n_gaba = np.sum(sl.data["synapses"][:, 6] == gaba_id)
n_ampa = np.sum(sl.data["synapses"][:, 6] == ampa_id)
self.assertTrue((20 * 8 + 10 * 2) * 0.5 -
10 < n_gaba < (20 * 8 + 10 * 2) * 0.5 + 10)
self.assertTrue((20 * 8 + 10 * 2) * 0.9 -
10 < n_ampa < (20 * 8 + 10 * 2) * 0.9 + 10)
with self.subTest("synapse-softmax"):
# Test of softmax
testing_config_file = os.path.join(
self.network_path, "network-config-test-2.json"
) # Only GABA synapses in this config
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# Softmax reduces number of synapses
self.assertTrue(sl.data["nSynapses"] < 20 * 8 + 10 * 2)
with self.subTest("synapse-mu2"):
# Test of mu2
testing_config_file = os.path.join(self.network_path,
"network-config-test-3.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# With mu2 having 2 synapses means 50% chance to keep them, having 1 will be likely to have it removed
self.assertTrue(
20 * 8 * 0.5 - 10 < sl.data["nSynapses"] < 20 * 8 * 0.5 + 10)
with self.subTest("synapse-a3"):
# Test of a3
testing_config_file = os.path.join(self.network_path,
"network-config-test-4.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# a3=0.6 means 40% chance to remove all synapses between a pair
self.assertTrue(
(20 * 8 + 10 * 2) * 0.6 -
14 < sl.data["nSynapses"] < (20 * 8 + 10 * 2) * 0.6 + 14)
with self.subTest("synapse-distance-dependent-pruning"):
# Testing distance dependent pruning
testing_config_file = os.path.join(self.network_path,
"network-config-test-5.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# "1*(d >= 100e-6)" means we remove all synapses closer than 100 micrometers
self.assertEqual(sl.data["nSynapses"], 20 * 6)
self.assertTrue(
(sl.data["synapses"][:, 8] >=
100).all()) # Column 8 -- distance to soma in micrometers
# TODO: Need to do same test for Gap Junctions also -- but should be same results, since same codebase
with self.subTest("gap-junction-f1"):
# Test of f1
testing_config_file = os.path.join(self.network_path,
"network-config-test-6.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# Setting f1=0.7 in config should remove 30% of gap junctions, but does so randomly
self.assertTrue(
64 * 0.7 - 10 < sl.data["nGapJunctions"] < 64 * 0.7 + 10)
with self.subTest("gap-junction-softmax"):
# Test of softmax
testing_config_file = os.path.join(self.network_path,
"network-config-test-7.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# Softmax reduces number of synapses
self.assertTrue(sl.data["nGapJunctions"] < 16 * 2 + 10)
with self.subTest("gap-junction-mu2"):
# Test of mu2
testing_config_file = os.path.join(self.network_path,
"network-config-test-8.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output)
# With mu2 having 4 synapses means 50% chance to keep them, having 1 will be likely to have it removed
self.assertTrue(
64 * 0.5 - 10 < sl.data["nGapJunctions"] < 64 * 0.5 + 10)
with self.subTest("gap-junction-a3"):
# Test of a3
testing_config_file = os.path.join(self.network_path,
"network-config-test-9.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output, verbose=True)
# a3=0.7 means 30% chance to remove all synapses between a pair
self.assertTrue(
64 * 0.7 - 10 < sl.data["nGapJunctions"] < 64 * 0.7 + 10)
if False: # Distance dependent pruning currently not implemented for gap junctions
with self.subTest("gap-junction-distance-dependent-pruning"):
# Testing distance dependent pruning
testing_config_file = os.path.join(
self.network_path, "network-config-test-10.json")
sp = SnuddaPrune(network_path=self.network_path,
config_file=testing_config_file,
verbose=True,
keep_files=True) # Use default config file
sp.prune()
# Load the pruned data and check it
sl = SnuddaLoad(pruned_output, verbose=True)
# "1*(d <= 120e-6)" means we remove all synapses further away than 100 micrometers
self.assertEqual(sl.data["nGapJunctions"], 2 * 4 * 4)
self.assertTrue(
(sl.data["gapJunctions"][:, 8] <=
120).all()) # Column 8 -- distance to soma in micrometers