def get_cell_muscle_names_and_connection(test=False):
spreadsheet_location = "../../../../" if test else "../../../"
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet(spreadsheet_location)
return mneurons, all_muscles, muscle_conns
def get_cell_muscle_names_and_connection(test=False):
spreadsheet_location = os.path.dirname(os.path.abspath(__file__))+"/../../../"
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet()
return mneurons, all_muscles, muscle_conns
def setUpClass(cls):
# read data from spread sheet
cls.cell_names, cls.conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../../")
print (str(len(cls.cell_names))+' unique cell names in CElegensNeuronTables')
# generate all nml files once
for index in cls.conns:
origin = index.pre_cell
target = index.post_cell
fn = origin+"_"+target
fnswap = target+"_"+origin
if fn not in cls.checked_files:
if fnswap not in cls.checked_files:
# generate xml and nml file
cells_to_plot = "["+origin+","+target+"]"
cells_to_stimulate = "["+origin+"]"
generate(fn, params, cells=cells_to_plot, cells_to_stimulate=cells_to_stimulate, duration=500, validate=False, test=True)
cls.checked_files.append(fn)
cls.counter += 1
print "Total files generated %i"%cls.counter
def setUpClass(cls):
# read data from spread sheet
cls.cell_names, cls.conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../../")
print (str(len(cls.cell_names))+' unique cell names in CElegensNeuronTables')
# generate all nml files once
for index in cls.conns:
origin = index.pre_cell
target = index.post_cell
fn = origin+"_"+target
fnswap = target+"_"+origin
if fn not in cls.checked_files:
if fnswap not in cls.checked_files:
# generate xml and nml file
cells_to_plot = "["+origin+","+target+"]"
cells_to_stimulate = "["+origin+"]"
generate(fn, params, cells=cells_to_plot, cells_to_stimulate=cells_to_stimulate, duration=500, validate=False, test=True)
cls.checked_files.append(fn)
cls.counter += 1
print "Total files generated %i"%cls.counter
def get_cell_muscle_names_and_connection(test=False):
spreadsheet_location = os.path.dirname(os.path.abspath(__file__))+"/../../../"
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet()
return mneurons, all_muscles, muscle_conns
def setUpClass(cls):
# generate files from pairs in CElegensNeuronTables
cls.cell_names, cls.conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../../")
print (str(len(cls.cell_names))+' unique cell names in CElegensNeuronTables')
for index in cls.conns:
origin = index.pre_cell
target = index.post_cell
fn = origin+"_"+target
fnswap = target+"_"+origin
if fn not in cls.checked_files:
if fnswap not in cls.checked_files:
# generate xml and nml file
cells_to_plot = "["+origin+","+target+"]"
cells_to_stimulate = "["+origin+"]"
generate(fn, params, cells=cells_to_plot, cells_to_stimulate=cells_to_stimulate, duration=500, test=True)
cls.checked_files.append(fn)
cls.counter += 1
# generate files from pairs not present in CElegensNeuronTables but in NeuronConnect.xls
cls.neuron_cell_names, cls.neuron_conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../../", neuron_connect=True)
print (str(len(cls.neuron_cell_names))+' unique cell names in NeuronConnectFormatted')
for index in cls.neuron_conns:
origin = index.pre_cell
target = index.post_cell
fn = origin+"_"+target
fnswap = target+"_"+origin
if fn not in cls.checked_files:
if fnswap not in cls.checked_files:
# generate xml and nml file
cells_to_plot = "["+origin+","+target+"]"
cells_to_stimulate = "["+origin+"]"
generate(fn, params, cells=cells_to_plot, cells_to_stimulate=cells_to_stimulate, duration=500, test=True)
cls.checked_files.append(fn)
cls.counter += 1
print ("Total files generated %i"%cls.counter)
def get_cell_names_and_connection(test=False):
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
# If called from unittest folder ammend path to "../../../../"
spreadsheet_location = "../../../../" if test else "../../../"
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet(spreadsheet_location, include_nonconnected_cells=True)
cell_names.sort()
return cell_names, conns
def generate(net_id, params, cells = None, cells_to_plot=None, cells_to_stimulate=None, duration=500, dt=0.01, vmin=-75, vmax=20):
nml_doc = NeuroMLDocument(id=net_id)
nml_doc.iaf_cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../")
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["cells"] = []
for cell in cell_names:
if cells is None or cell in cells:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
inst = Instance(id="0")
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file = '../../generatedNeuroML2/%s.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
print("Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"%(cell_file, all_cells[cell].id, location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y), float(location.z))
exp_input = ExplicitInput(target="%s/0/%s"%(pop0.id, params.generic_cell.id),
input=params.offset_current.id)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
lems_info["plots"].append(plot)
lems_info["cells"].append(cell)
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
syn_new = create_n_connection_synapse(syn0, conn.number, nml_doc)
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
# Get the corresponding Cell for each
# pre_cell = all_cells[conn.pre_cell]
# post_cell = all_cells[conn.post_cell]
net.projections.append(proj0)
# Add a Connection with the closest locations
conn0 = Connection(id="0", \
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id),
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id))
proj0.connections.append(conn0)
write_to_file(nml_doc, lems_info, net_id)
def generate(net_id,
params,
cells = None,
cells_to_plot = None,
cells_to_stimulate = None,
include_muscles=False,
conn_number_override = None,
conn_number_scaling = None,
duration = 500,
dt = 0.01,
vmin = -75,
vmax = 20,
seed = 1234,
validate=True, test=False):
random.seed(seed)
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All cells")+\
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")+\
" Include muscles: %s\n" % include_muscles
info += "\n%s\n"%(bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
nml_doc.iaf_cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
# If called from unittest folder ammend path to "../../../../"
spreadsheet_location = "../../../../" if test else "../../../"
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet(spreadsheet_location, include_nonconnected_cells=True)
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["activity_plots"] = []
lems_info["muscle_plots"] = []
lems_info["muscle_activity_plots"] = []
lems_info["to_save"] = []
lems_info["activity_to_save"] = []
lems_info["muscles_to_save"] = []
lems_info["muscles_activity_to_save"] = []
lems_info["cells"] = []
lems_info["muscles"] = []
lems_info["includes"] = []
if hasattr(params.generic_cell, 'custom_component_type_definition'):
lems_info["includes"].append(params.generic_cell.custom_component_type_definition)
backers_dir = "../../../../OpenWormBackers/" if test else "../../../OpenWormBackers/"
sys.path.append(backers_dir)
import backers
cells_vs_name = backers.get_adopted_cell_names(backers_dir)
populations_without_location = isinstance(params.elec_syn, GapJunction)
count = 0
for cell in cell_names:
if cells is None or cell in cells:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(cell):
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[cell])
pop0.properties.append(p)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file_path = "../../../" if test else "../../" #if running test
cell_file = cell_file_path+'generatedNeuroML2/%s.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
print("Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"%(cell_file, all_cells[cell].id, location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y), float(location.z))
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (cell)
exp_input = ExplicitInput(target=target, input=params.offset_current.id)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (cell)
lems_info["plots"].append(plot)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_plots"].append(plot)
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (cell)
lems_info["to_save"].append(save)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_to_save"].append(save)
lems_info["cells"].append(cell)
count+=1
print("Finished loading %i cells"%count)
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet(spreadsheet_location)
muscles = get_muscle_names()
if include_muscles:
muscle_count = 0
for muscle in muscles:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(muscle):
# No muscles adopted yet, but just in case they are in future...
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[muscle])
pop0.properties.append(p)
inst.location = Location(100, 10*muscle_count, 100)
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (muscle)
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscle_plots"].append(plot)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscle_activity_plots"].append(plot)
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscles_to_save"].append(save)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscles_activity_to_save"].append(save)
lems_info["muscles"].append(muscle)
muscle_count+=1
print("Finished creating %i muscles"%muscle_count)
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
if include_muscles:
for conn in muscle_conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in muscles:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
# import pprint
# pprint.pprint(lems_info)
template_path = '../' if test else '' # if running test
write_to_file(nml_doc, lems_info, net_id, template_path, validate=validate)
return nml_doc
# Get the standard name for a network connection as used in the neuroConstruct project
def get_projection_id(pre, post, syntype):
proj_id = "NCXLS_%s_%s"%(pre, post)
if "GapJunction" in syntype:
proj_id += '_GJ'
return proj_id
if __name__ == "__main__":
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet()
net_id = "CElegansConnectome"
nml_network_doc = NeuroMLDocument(id=net_id)
# Create a NeuroML Network data structure to hold on to all the connection info.
net = Network(id=net_id)
nml_network_doc.networks.append(net)
# To hold all Cell NeuroML objects vs. names
all_cells = {}
for cell in cell_names:
# build a Population data structure out of the cell name
pop0 = Population(id=cell, component=cell, size=1)
def generate(net_id,
params,
cells = None,
cells_to_plot = None,
cells_to_stimulate = None,
include_muscles=False,
conn_number_override = None,
conn_number_scaling = None,
duration = 500,
dt = 0.01,
vmin = None,
vmax = None,
seed = 1234,
validate=True,
test=False,
verbose=True,
target_directory='./'):
params.create_models()
if vmin==None:
if params.level == 'A':
vmin=-72
elif params.level == 'B':
vmin=-52
elif params.level == 'C':
vmin=-60
if vmax==None:
if params.level == 'A':
vmax=-48
elif params.level == 'B':
vmax=-28
elif params.level == 'C':
vmax=25
random.seed(seed)
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All cells")+\
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")+\
" Include muscles: %s\n" % include_muscles
info += "\n%s\n"%(params.bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
if params.level == "A" or params.level == "B":
nml_doc.iaf_cells.append(params.generic_cell)
elif params.level == "C":
nml_doc.cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
# If called from unittest folder ammend path to "../../../../"
spreadsheet_location = "../../../../" if test else "../../../"
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet(spreadsheet_location, include_nonconnected_cells=True)
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["activity_plots"] = []
lems_info["muscle_plots"] = []
lems_info["muscle_activity_plots"] = []
lems_info["to_save"] = []
lems_info["activity_to_save"] = []
lems_info["muscles_to_save"] = []
lems_info["muscles_activity_to_save"] = []
lems_info["cells"] = []
lems_info["muscles"] = []
lems_info["includes"] = []
if params.custom_component_types_definitions:
lems_info["includes"].append(params.custom_component_types_definitions)
if target_directory != './':
def_file = "%s/%s"%(os.path.dirname(__file__), params.custom_component_types_definitions)
shutil.copy(def_file, target_directory)
backers_dir = "../../../../OpenWormBackers/" if test else "../../../OpenWormBackers/"
sys.path.append(backers_dir)
import backers
cells_vs_name = backers.get_adopted_cell_names(backers_dir)
populations_without_location = isinstance(params.elec_syn, GapJunction)
count = 0
for cell in cell_names:
if cells is None or cell in cells:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(cell):
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[cell])
pop0.properties.append(p)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file_path = "../../../" if test else "../../" #if running test
cell_file = cell_file_path+'generatedNeuroML2/%s.cell.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
if verbose:
print("Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"%(cell_file, all_cells[cell].id, location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y), float(location.z))
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (cell)
exp_input = ExplicitInput(target=target, input=params.offset_current.id)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (cell)
lems_info["plots"].append(plot)
if params.generic_cell.__class__.__name__ == 'IafActivityCell':
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_plots"].append(plot)
if params.generic_cell.__class__.__name__ == 'Cell':
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/caConc" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/caConc" % (cell)
lems_info["activity_plots"].append(plot)
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (cell)
lems_info["to_save"].append(save)
if params.generic_cell.__class__.__name__ == 'IafActivityCell':
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_to_save"].append(save)
lems_info["cells"].append(cell)
count+=1
if verbose:
print("Finished loading %i cells"%count)
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet(spreadsheet_location)
muscles = get_muscle_names()
if include_muscles:
muscle_count = 0
for muscle in muscles:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(muscle):
# No muscles adopted yet, but just in case they are in future...
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[muscle])
pop0.properties.append(p)
inst.location = Location(100, 10*muscle_count, 100)
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (muscle)
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscle_plots"].append(plot)
if params.generic_cell.__class__.__name__ == 'IafActivityCell':
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscle_activity_plots"].append(plot)
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscles_to_save"].append(save)
if params.generic_cell.__class__.__name__ == 'IafActivityCell':
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscles_activity_to_save"].append(save)
lems_info["muscles"].append(muscle)
muscle_count+=1
if verbose:
print("Finished creating %i muscles"%muscle_count)
existing_synapses = {}
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
if verbose:
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc, existing_synapses)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
if include_muscles:
for conn in muscle_conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in muscles:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
if verbose:
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc, existing_synapses)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
# import pprint
# pprint.pprint(lems_info)
template_path = '../' if test else '' # if running test
write_to_file(nml_doc, lems_info, net_id, template_path, validate=validate, verbose=verbose, target_directory=target_directory)
return nml_doc
def generate(net_id, params, cells=None, cells_to_plot=None, cells_to_stimulate=None, conn_number_override=None, conn_number_scaling=None, duration=500, dt=0.01, vmin=-75, vmax=20):
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All cells")+\
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")
info += "\n%s\n"%(bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
nml_doc.iaf_cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../")
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["cells"] = []
for cell in cell_names:
if cells is None or cell in cells:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
inst = Instance(id="0")
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file = '../../generatedNeuroML2/%s.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
print("Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"%(cell_file, all_cells[cell].id, location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y), float(location.z))
exp_input = ExplicitInput(target="%s/0/%s"%(pop0.id, params.generic_cell.id),
input=params.offset_current.id)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
lems_info["plots"].append(plot)
lems_info["cells"].append(cell)
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc)
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
# Get the corresponding Cell for each
# pre_cell = all_cells[conn.pre_cell]
# post_cell = all_cells[conn.post_cell]
net.projections.append(proj0)
# Add a Connection with the closest locations
conn0 = Connection(id="0", \
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id),
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id))
proj0.connections.append(conn0)
write_to_file(nml_doc, lems_info, net_id)
def generate(
net_id, params, cells=None, cells_to_plot=None, cells_to_stimulate=None, duration=500, dt=0.01, vmin=-75, vmax=20
):
nml_doc = NeuroMLDocument(id=net_id)
nml_doc.iaf_cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../")
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id,
}
lems_info["plots"] = []
lems_info["cells"] = []
for cell in cell_names:
if cells is None or cell in cells:
# build a Population data structure out of the cell name
pop0 = Population(id=cell, component=params.generic_cell.id, type="populationList")
inst = Instance(id="0")
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file = "../../generatedNeuroML2/%s.nml" % cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
print(
"Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"
% (cell_file, all_cells[cell].id, location.x, location.y, location.z)
)
inst.location = Location(float(location.x), float(location.y), float(location.z))
exp_input = ExplicitInput(
target="%s/0/%s" % (pop0.id, params.generic_cell.id), input=params.offset_current.id
)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
lems_info["plots"].append(plot)
lems_info["cells"].append(cell)
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
syn0 = params.exc_syn
if "GABA" in conn.synclass:
syn0 = params.inh_syn
syn_new = create_n_connection_synapse(syn0, conn.number, nml_doc)
proj0 = Projection(
id=proj_id,
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id,
)
# Get the corresponding Cell for each
# pre_cell = all_cells[conn.pre_cell]
# post_cell = all_cells[conn.post_cell]
net.projections.append(proj0)
# Add a Connection with the closest locations
conn0 = Connection(
id="0",
pre_cell_id="../%s/0/%s" % (conn.pre_cell, params.generic_cell.id),
post_cell_id="../%s/0/%s" % (conn.post_cell, params.generic_cell.id),
)
proj0.connections.append(conn0)
write_to_file(nml_doc, lems_info, net_id)
