def generatePopulationSimulationLEMS(n_pops, baseline, pops, duration, dt, dl):
# Create simulation
# Create LEMS file
dl_str = 'DL' if dl else ''
sim_file = 'LEMS_WC_%s%s.xml' % (baseline, dl_str)
sim_id = 'WC_%s%s' % (baseline, dl_str)
ls = LEMSSimulation(sim_id, duration, dt, 'net1')
colours = ['#ff0000', '#0000ff']
colours2 = ['#ff7777', '#7777ff']
# Add additional LEMS files
# Add Wilson and Cowan Components
ls.include_lems_file('WC_Parameters%s.xml' % dl_str, include_included=True)
# Add the network definition
ls.include_lems_file('WC_%s%s.net.nml' % (baseline, dl_str),
include_included=True)
ls.set_report_file('report.txt')
disp2 = 'd2'
ls.create_display(disp2, 'Rates', -.1, 1.2)
for pop_idx, pop in enumerate(pops):
for n_pop in range(n_pops[pop_idx]):
ls.add_line_to_display(disp2,
'r_%s' % pop,
'%sPop/%d/%s/%s' %
(pop, n_pop, pop, 'R' if dl else 'r'),
color=colours[pop_idx])
disp1 = 'd1'
ls.create_display(disp1, 'iSyn', -2, 8)
for pop_idx, pop in enumerate(pops):
for n_pop in range(n_pops[pop_idx]):
ls.add_line_to_display(disp1,
'iSyn_%s' % pop,
'%sPop/%d/%s/iSyn' % (pop, n_pop, pop),
color=colours[pop_idx],
scale=1 if dl else '1nA')
ls.add_line_to_display(disp1,
'f_%s' % pop,
'%sPop/%d/%s/f' % (pop, n_pop, pop),
color=colours2[pop_idx])
of1 = 'of_%s' % pop
ls.create_output_file(of1, 'WC_%s%s.dat' % (baseline, dl_str))
for pop_idx, pop in enumerate(pops):
# save rates in output file
for n_pop in range(n_pops[pop_idx]):
ls.add_column_to_output_file(
of1, 'r_%s' % pop,
'%sPop/%d/%s/%s' % (pop, n_pop, pop, 'R' if dl else 'r'))
save_path = os.path.join(sim_file)
ls.save_to_file(file_name=save_path)
def generate_lems_file_for_neuroml(
sim_id,
neuroml_file,
target,
duration,
dt,
lems_file_name,
target_dir,
nml_doc=None, # Use this if the nml doc has already been loaded (to avoid delay in reload)
include_extra_files=[],
gen_plots_for_all_v=True,
plot_all_segments=False,
gen_plots_for_quantities={}, # Dict with displays vs lists of quantity paths
gen_plots_for_only_populations=[], # List of populations, all pops if=[]
gen_saves_for_all_v=True,
save_all_segments=False,
gen_saves_for_only_populations=[], # List of populations, all pops if=[]
gen_saves_for_quantities={}, # Dict with file names vs lists of quantity paths
gen_spike_saves_for_all_somas=False,
gen_spike_saves_for_only_populations=[], # List of populations, all pops if=[]
gen_spike_saves_for_cells={}, # Dict with file names vs lists of quantity paths
spike_time_format='ID_TIME',
copy_neuroml=True,
report_file_name=None,
lems_file_generate_seed=None,
verbose=False,
simulation_seed=12345):
my_random = random.Random()
if lems_file_generate_seed:
my_random.seed(
lems_file_generate_seed
) # To ensure same LEMS file (e.g. colours of plots) are generated every time for the same input
else:
my_random.seed(
12345
) # To ensure same LEMS file (e.g. colours of plots) are generated every time for the same input
file_name_full = '%s/%s' % (target_dir, lems_file_name)
print_comment_v(
'Creating LEMS file at: %s for NeuroML 2 file: %s (copy: %s)' %
(file_name_full, neuroml_file, copy_neuroml))
ls = LEMSSimulation(sim_id,
duration,
dt,
target,
simulation_seed=simulation_seed)
if nml_doc is None:
nml_doc = read_neuroml2_file(neuroml_file,
include_includes=True,
verbose=verbose)
nml_doc_inc_not_included = read_neuroml2_file(neuroml_file,
include_includes=False,
verbose=False)
else:
nml_doc_inc_not_included = nml_doc
ls.set_report_file(report_file_name)
quantities_saved = []
for f in include_extra_files:
ls.include_neuroml2_file(f, include_included=False)
if not copy_neuroml:
rel_nml_file = os.path.relpath(os.path.abspath(neuroml_file),
os.path.abspath(target_dir))
print_comment_v("Including existing NeuroML file (%s) as: %s" %
(neuroml_file, rel_nml_file))
ls.include_neuroml2_file(rel_nml_file,
include_included=True,
relative_to_dir=os.path.abspath(target_dir))
else:
print_comment_v(
"Copying a NeuroML file (%s) to: %s (abs path: %s)" %
(neuroml_file, target_dir, os.path.abspath(target_dir)))
if not os.path.isdir(target_dir):
raise Exception("Target directory %s does not exist!" % target_dir)
if os.path.realpath(
os.path.dirname(neuroml_file)) != os.path.realpath(target_dir):
shutil.copy(neuroml_file, target_dir)
else:
print_comment_v("No need, same file...")
neuroml_file_name = os.path.basename(neuroml_file)
ls.include_neuroml2_file(neuroml_file_name, include_included=False)
nml_dir = os.path.dirname(neuroml_file) if len(
os.path.dirname(neuroml_file)) > 0 else '.'
for include in nml_doc_inc_not_included.includes:
if nml_dir == '.' and os.path.isfile(include.href):
incl_curr = include.href
else:
incl_curr = '%s/%s' % (nml_dir, include.href)
if os.path.isfile(include.href):
incl_curr = include.href
print_comment_v(
' - Including %s (located at %s; nml dir: %s), copying to %s' %
(include.href, incl_curr, nml_dir, target_dir))
'''
if not os.path.isfile("%s/%s" % (target_dir, os.path.basename(incl_curr))) and \
not os.path.isfile("%s/%s" % (target_dir, incl_curr)) and \
not os.path.isfile(incl_curr):
shutil.copy(incl_curr, target_dir)
else:
print_comment_v("No need to copy...")'''
f1 = "%s/%s" % (target_dir, os.path.basename(incl_curr))
f2 = "%s/%s" % (target_dir, incl_curr)
if os.path.isfile(f1):
print_comment_v("No need to copy, file exists: %s..." % f1)
elif os.path.isfile(f2):
print_comment_v("No need to copy, file exists: %s..." % f2)
else:
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
sub_doc = read_neuroml2_file(incl_curr)
sub_dir = os.path.dirname(incl_curr) if len(
os.path.dirname(incl_curr)) > 0 else '.'
if sub_doc.__class__ == neuroml.nml.nml.NeuroMLDocument:
for include in sub_doc.includes:
incl_curr = '%s/%s' % (sub_dir, include.href)
print_comment_v(' -- Including %s located at %s' %
(include.href, incl_curr))
if not os.path.isfile("%s/%s" % (target_dir, os.path.basename(incl_curr))) and \
not os.path.isfile("%s/%s" % (target_dir, incl_curr)):
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href,
include_included=False)
if gen_plots_for_all_v \
or gen_saves_for_all_v \
or len(gen_plots_for_only_populations) > 0 \
or len(gen_saves_for_only_populations) > 0 \
or gen_spike_saves_for_all_somas \
or len(gen_spike_saves_for_only_populations) > 0:
for network in nml_doc.networks:
for population in network.populations:
variable = "v"
quantity_template_e = "%s[%i]"
component = population.component
size = population.size
cell = None
segment_ids = []
for c in nml_doc.spike_generator_poissons:
if c.id == component:
variable = "tsince"
for c in nml_doc.SpikeSourcePoisson:
if c.id == component:
variable = "tsince"
quantity_template = "%s[%i]/" + variable
if plot_all_segments or gen_spike_saves_for_all_somas:
for c in nml_doc.cells:
if c.id == component:
cell = c
for segment in cell.morphology.segments:
segment_ids.append(segment.id)
segment_ids.sort()
if population.type and population.type == 'populationList':
quantity_template = "%s/%i/" + component + "/" + variable
quantity_template_e = "%s/%i/" + component + ""
# Multicompartmental cell
# Needs to be supported in NeuronWriter
# if len(segment_ids)>1:
# quantity_template_e = "%s/%i/"+component+"/0"
size = len(population.instances)
if gen_plots_for_all_v or population.id in gen_plots_for_only_populations:
print_comment(
'Generating %i plots for %s in population %s' %
(size, component, population.id))
disp0 = 'DispPop__%s' % population.id
ls.create_display(
disp0,
"Membrane potentials of cells in %s" % population.id,
"-90", "50")
for i in range(size):
if cell is not None and plot_all_segments:
quantity_template_seg = "%s/%i/" + component + "/%i/v"
for segment_id in segment_ids:
quantity = quantity_template_seg % (
population.id, i, segment_id)
ls.add_line_to_display(
disp0, "%s[%i] seg %i: v" %
(population.id, i, segment_id), quantity,
"1mV", get_next_hex_color(my_random))
else:
quantity = quantity_template % (population.id, i)
ls.add_line_to_display(
disp0, "%s[%i]: v" % (population.id, i),
quantity, "1mV", get_next_hex_color(my_random))
if gen_saves_for_all_v or population.id in gen_saves_for_only_populations:
print_comment(
'Saving %i values of %s for %s in population %s' %
(size, variable, component, population.id))
of0 = 'Volts_file__%s' % population.id
ls.create_output_file(
of0,
"%s.%s.%s.dat" % (sim_id, population.id, variable))
for i in range(size):
if cell is not None and save_all_segments:
quantity_template_seg = "%s/%i/" + component + "/%i/v"
for segment_id in segment_ids:
quantity = quantity_template_seg % (
population.id, i, segment_id)
ls.add_column_to_output_file(
of0, 'v_%s' % safe_variable(quantity),
quantity)
quantities_saved.append(quantity)
else:
quantity = quantity_template % (population.id, i)
ls.add_column_to_output_file(
of0, 'v_%s' % safe_variable(quantity),
quantity)
quantities_saved.append(quantity)
if gen_spike_saves_for_all_somas or population.id in gen_spike_saves_for_only_populations:
print_comment(
'Saving spikes in %i somas for %s in population %s' %
(size, component, population.id))
eof0 = 'Spikes_file__%s' % population.id
ls.create_event_output_file(eof0,
"%s.%s.spikes" %
(sim_id, population.id),
format=spike_time_format)
for i in range(size):
quantity = quantity_template_e % (population.id, i)
ls.add_selection_to_event_output_file(
eof0, i, quantity, "spike")
quantities_saved.append(quantity)
for display in sorted(gen_plots_for_quantities.keys()):
quantities = gen_plots_for_quantities[display]
max_ = "1"
min_ = "-1"
scale = "1"
# Check for v ...
if quantities and len(quantities) > 0 and quantities[0].endswith('/v'):
max_ = "40"
min_ = "-80"
scale = "1mV"
ls.create_display(display, "Plots of %s" % display, min_, max_)
for q in quantities:
ls.add_line_to_display(display, safe_variable(q), q, scale,
get_next_hex_color(my_random))
for file_name in sorted(gen_saves_for_quantities.keys()):
quantities = gen_saves_for_quantities[file_name]
of_id = safe_variable(file_name)
ls.create_output_file(of_id, file_name)
for q in quantities:
ls.add_column_to_output_file(of_id, safe_variable(q), q)
quantities_saved.append(q)
for file_name in sorted(gen_spike_saves_for_cells.keys()):
quantities = gen_spike_saves_for_cells[file_name]
of_id = safe_variable(file_name)
ls.create_event_output_file(of_id, file_name)
pop_here = None
for i, quantity in enumerate(quantities):
pop, index = get_pop_index(quantity)
if pop_here:
if pop_here != pop:
raise Exception('Problem with generating LEMS for saving spikes for file %s.\n' % file_name + \
'Multiple cells from different populations in one file will cause issues with index/spike id.')
pop_here = pop
# print('===== Adding to %s (%s) event %i for %s, pop: %s, i: %s' % (file_name, of_id, i, quantity, pop, index))
ls.add_selection_to_event_output_file(of_id, index, quantity,
"spike")
quantities_saved.append(quantity)
ls.save_to_file(file_name=file_name_full)
return quantities_saved, ls
def generate_lems_file_for_neuroml(sim_id,
neuroml_file,
target,
duration,
dt,
lems_file_name,
target_dir,
nml_doc = None, # Use this if the nml doc has already been loaded (to avoid delay in reload)
include_extra_files = [],
gen_plots_for_all_v = True,
plot_all_segments = False,
gen_plots_for_quantities = {}, # Dict with displays vs lists of quantity paths
gen_plots_for_only_populations = [], # List of populations, all pops if = []
gen_saves_for_all_v = True,
save_all_segments = False,
gen_saves_for_only_populations = [], # List of populations, all pops if = []
gen_saves_for_quantities = {}, # Dict with file names vs lists of quantity paths
gen_spike_saves_for_all_somas = False,
gen_spike_saves_for_only_populations = [], # List of populations, all pops if = []
gen_spike_saves_for_cells = {}, # Dict with file names vs lists of quantity paths
spike_time_format='ID_TIME',
copy_neuroml = True,
report_file_name = None,
lems_file_generate_seed=None,
verbose=False,
simulation_seed=12345):
my_random = random.Random()
if lems_file_generate_seed:
my_random.seed(lems_file_generate_seed) # To ensure same LEMS file (e.g. colours of plots) are generated every time for the same input
else:
my_random.seed(12345) # To ensure same LEMS file (e.g. colours of plots) are generated every time for the same input
file_name_full = '%s/%s'%(target_dir,lems_file_name)
print_comment_v('Creating LEMS file at: %s for NeuroML 2 file: %s (copy: %s)'%(file_name_full,neuroml_file,copy_neuroml))
ls = LEMSSimulation(sim_id, duration, dt, target,simulation_seed=simulation_seed)
if nml_doc == None:
nml_doc = read_neuroml2_file(neuroml_file, include_includes=True, verbose=verbose)
nml_doc_inc_not_included = read_neuroml2_file(neuroml_file, include_includes=False, verbose=False)
else:
nml_doc_inc_not_included = nml_doc
ls.set_report_file(report_file_name)
quantities_saved = []
for f in include_extra_files:
ls.include_neuroml2_file(f, include_included=False)
if not copy_neuroml:
rel_nml_file = os.path.relpath(os.path.abspath(neuroml_file), os.path.abspath(target_dir))
print_comment_v("Including existing NeuroML file (%s) as: %s"%(neuroml_file, rel_nml_file))
ls.include_neuroml2_file(rel_nml_file, include_included=True, relative_to_dir=os.path.abspath(target_dir))
else:
print_comment_v("Copying a NeuroML file (%s) to: %s (abs path: %s)"%(neuroml_file, target_dir, os.path.abspath(target_dir)))
if not os.path.isdir(target_dir):
raise Exception("Target directory %s does not exist!"%target_dir)
if os.path.realpath(os.path.dirname(neuroml_file))!=os.path.realpath(target_dir):
shutil.copy(neuroml_file, target_dir)
else:
print_comment_v("No need, same file...")
neuroml_file_name = os.path.basename(neuroml_file)
ls.include_neuroml2_file(neuroml_file_name, include_included=False)
nml_dir = os.path.dirname(neuroml_file) if len(os.path.dirname(neuroml_file))>0 else '.'
for include in nml_doc_inc_not_included.includes:
if nml_dir=='.' and os.path.isfile(include.href):
incl_curr = include.href
else:
incl_curr = '%s/%s'%(nml_dir,include.href)
if os.path.isfile(include.href):
incl_curr = include.href
print_comment_v(' - Including %s (located at %s; nml dir: %s), copying to %s'%(include.href, incl_curr, nml_dir, target_dir))
'''
if not os.path.isfile("%s/%s"%(target_dir, os.path.basename(incl_curr))) and \
not os.path.isfile("%s/%s"%(target_dir, incl_curr)) and \
not os.path.isfile(incl_curr):
shutil.copy(incl_curr, target_dir)
else:
print_comment_v("No need to copy...")'''
f1 = "%s/%s"%(target_dir, os.path.basename(incl_curr))
f2 = "%s/%s"%(target_dir, incl_curr)
if os.path.isfile(f1):
print_comment_v("No need to copy, file exists: %s..."%f1)
elif os.path.isfile(f2):
print_comment_v("No need to copy, file exists: %s..."%f2)
else:
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
sub_doc = read_neuroml2_file(incl_curr)
sub_dir = os.path.dirname(incl_curr) if len(os.path.dirname(incl_curr))>0 else '.'
for include in sub_doc.includes:
incl_curr = '%s/%s'%(sub_dir,include.href)
print_comment_v(' -- Including %s located at %s'%(include.href, incl_curr))
if not os.path.isfile("%s/%s"%(target_dir, os.path.basename(incl_curr))) and \
not os.path.isfile("%s/%s"%(target_dir, incl_curr)):
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
if gen_plots_for_all_v \
or gen_saves_for_all_v \
or len(gen_plots_for_only_populations)>0 \
or len(gen_saves_for_only_populations)>0 \
or gen_spike_saves_for_all_somas \
or len(gen_spike_saves_for_only_populations)>0 :
for network in nml_doc.networks:
for population in network.populations:
variable = "v" #
quantity_template_e = "%s[%i]"
component = population.component
size = population.size
cell = None
segment_ids = []
for c in nml_doc.spike_generator_poissons:
if c.id == component:
variable = "tsince"
for c in nml_doc.SpikeSourcePoisson:
if c.id == component:
variable = "tsince"
quantity_template = "%s[%i]/"+variable
if plot_all_segments or gen_spike_saves_for_all_somas:
for c in nml_doc.cells:
if c.id == component:
cell = c
for segment in cell.morphology.segments:
segment_ids.append(segment.id)
segment_ids.sort()
if population.type and population.type == 'populationList':
quantity_template = "%s/%i/"+component+"/"+variable
quantity_template_e = "%s/%i/"+component+""
# Multicompartmental cell
### Needs to be supported in NeuronWriter
###if len(segment_ids)>1:
### quantity_template_e = "%s/%i/"+component+"/0"
size = len(population.instances)
if gen_plots_for_all_v or population.id in gen_plots_for_only_populations:
print_comment('Generating %i plots for %s in population %s'%(size, component, population.id))
disp0 = 'DispPop__%s'%population.id
ls.create_display(disp0, "Membrane potentials of cells in %s"%population.id, "-90", "50")
for i in range(size):
if cell!=None and plot_all_segments:
quantity_template_seg = "%s/%i/"+component+"/%i/v"
for segment_id in segment_ids:
quantity = quantity_template_seg%(population.id, i, segment_id)
ls.add_line_to_display(disp0, "%s[%i] seg %i: v"%(population.id, i, segment_id), quantity, "1mV", get_next_hex_color(my_random))
else:
quantity = quantity_template%(population.id, i)
ls.add_line_to_display(disp0, "%s[%i]: v"%(population.id, i), quantity, "1mV", get_next_hex_color(my_random))
if gen_saves_for_all_v or population.id in gen_saves_for_only_populations:
print_comment('Saving %i values of %s for %s in population %s'%(size, variable, component, population.id))
of0 = 'Volts_file__%s'%population.id
ls.create_output_file(of0, "%s.%s.%s.dat"%(sim_id,population.id,variable))
for i in range(size):
if cell!=None and save_all_segments:
quantity_template_seg = "%s/%i/"+component+"/%i/v"
for segment_id in segment_ids:
quantity = quantity_template_seg%(population.id, i, segment_id)
ls.add_column_to_output_file(of0, 'v_%s'%safe_variable(quantity), quantity)
quantities_saved.append(quantity)
else:
quantity = quantity_template%(population.id, i)
ls.add_column_to_output_file(of0, 'v_%s'%safe_variable(quantity), quantity)
quantities_saved.append(quantity)
if gen_spike_saves_for_all_somas or population.id in gen_spike_saves_for_only_populations:
print_comment('Saving spikes in %i somas for %s in population %s'%(size, component, population.id))
eof0 = 'Spikes_file__%s'%population.id
ls.create_event_output_file(eof0, "%s.%s.spikes"%(sim_id,population.id), format=spike_time_format)
for i in range(size):
quantity = quantity_template_e%(population.id, i)
ls.add_selection_to_event_output_file(eof0, i, quantity, "spike")
quantities_saved.append(quantity)
for display in gen_plots_for_quantities.keys():
quantities = gen_plots_for_quantities[display]
max_ = "1"
min_ = "-1"
scale = "1"
# Check for v ...
if quantities and len(quantities)>0 and quantities[0].endswith('/v'):
max_ = "40"
min_ = "-80"
scale = "1mV"
ls.create_display(display, "Plots of %s"%display, min_, max_)
for q in quantities:
ls.add_line_to_display(display, safe_variable(q), q, scale, get_next_hex_color(my_random))
for file_name in gen_saves_for_quantities.keys():
quantities = gen_saves_for_quantities[file_name]
of_id = safe_variable(file_name)
ls.create_output_file(of_id, file_name)
for q in quantities:
ls.add_column_to_output_file(of_id, safe_variable(q), q)
quantities_saved.append(q)
for file_name in gen_spike_saves_for_cells.keys():
cells = gen_spike_saves_for_cells[file_name]
of_id = safe_variable(file_name)
ls.create_event_output_file(of_id, file_name)
for i, c in enumerate(cells):
ls.add_selection_to_event_output_file(of_id, i, c, "spike")
quantities_saved.append(c)
ls.save_to_file(file_name=file_name_full)
return quantities_saved, ls
