def save_to_file(self, file_name=None):
if file_name is None:
file_name = "LEMS_%s.xml" % self.lems_info['sim_id']
lems_file = open(file_name, 'w')
lems_file.write(self.to_xml())
lems_file.close()
print_comment("Written LEMS Simulation %s to file: %s" % (self.lems_info['sim_id'], file_name), True)
return file_name
def save_to_file(self, file_name=None):
if file_name==None:
file_name = "LEMS_%s.xml"%self.lems_info['sim_id']
lems_file = open(file_name, 'w')
lems_file.write(self.to_xml())
lems_file.close()
print_comment("Written LEMS Simulation %s to file: %s"%(self.lems_info['sim_id'], file_name), True)
return file_name
def generate_lems_channel_analyser(channel_file, channel, min_target_voltage,
step_target_voltage, max_target_voltage, clamp_delay,
clamp_duration, clamp_base_voltage, duration, erev,
gates, temperature, ca_conc, iv_curve, scale_dt=1,
dat_suffix='',verbose=True):
print_comment(("Generating LEMS file to investigate %s in %s, %smV->%smV, "
"%sdegC") % (channel,channel_file, min_target_voltage,
max_target_voltage, temperature), verbose)
target_voltages = []
v = min_target_voltage
while v <= max_target_voltage:
target_voltages.append(v)
v+=step_target_voltage
target_voltages_map = []
for t in target_voltages:
fract = float(target_voltages.index(t)) / (len(target_voltages)-1)
info = {}
info["v"] = t
info["v_str"] = str(t).replace("-", "min")
info["col"] = get_colour_hex(fract)
target_voltages_map.append(info)
includes = get_includes_from_channel_file(channel_file)
includes_relative = []
base_path = os.path.dirname(channel_file)
for inc in includes:
includes_relative.append(os.path.abspath(base_path+'/'+inc))
model = {"channel_file": channel_file,
"includes": includes_relative,
"channel": channel,
"target_voltages" : target_voltages_map,
"clamp_delay": clamp_delay,
"clamp_duration": clamp_duration,
"clamp_base_voltage": clamp_base_voltage,
"min_target_voltage": min_target_voltage,
"max_target_voltage": max_target_voltage,
"duration": duration,
"scale_dt": scale_dt,
"erev": erev,
"gates": gates,
"temperature": temperature,
"ca_conc": ca_conc,
"iv_curve": iv_curve,
"dat_suffix": dat_suffix}
#pp.pprint(model)
merged = merge_with_template(model, TEMPLATE_FILE)
return merged
def save_to_file(self, file_name=None):
if file_name == None:
file_name = "LEMS_%s.xml" % self.lems_info['sim_id']
lems_file = open(file_name, 'w')
lems_file.write(self.to_xml())
lems_file.close()
print_comment(
"Written LEMS Simulation %s to file: %s" %
(self.lems_info['sim_id'], file_name), True)
return file_name
# main method example moved to examples/create_new_lems_file.py
def show(self):
"""
Plot the result of the simulation once it's been intialized
"""
from matplotlib import pyplot as plt
if self.already_run:
for ref in self.volts.keys():
plt.plot(self.t, self.volts[ref], label=ref)
plt.title("Simulation voltage vs time")
plt.legend()
plt.xlabel("Time [ms]")
plt.ylabel("Voltage [mV]")
else:
pynml.print_comment("First you have to 'go()' the simulation.", True)
plt.show()
def show(self):
"""
Plot the result of the simulation once it's been intialized
"""
from matplotlib import pyplot as plt
if self.already_run:
for ref in self.volts.keys():
plt.plot(self.t, self.volts[ref], label=ref)
plt.title("Simulation voltage vs time")
plt.legend()
plt.xlabel("Time [ms]")
plt.ylabel("Voltage [mV]")
else:
pynml.print_comment("First you have to 'go()' the simulation.",
True)
plt.show()
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA=-0.1,
end_amp_nA=0.1,
step_nA=0.01,
custom_amps_nA=[],
analysis_duration=1000,
analysis_delay=0,
pre_zero_pulse=0,
post_zero_pulse=0,
dt=0.05,
temperature="32degC",
spike_threshold_mV=0.,
plot_voltage_traces=False,
plot_if=True,
plot_iv=False,
xlim_if=None,
ylim_if=None,
xlim_iv=None,
ylim_iv=None,
label_xaxis=True,
label_yaxis=True,
show_volts_label=True,
grid=True,
font_size=12,
if_iv_color='k',
linewidth=1,
bottom_left_spines_only=False,
show_plot_already=True,
save_voltage_traces_to=None,
save_if_figure_to=None,
save_iv_figure_to=None,
save_if_data_to=None,
save_iv_data_to=None,
simulator="jNeuroML",
num_processors=1,
include_included=True,
title_above_plot=False,
return_axes=False,
verbose=False):
print_comment(
"Running generate_current_vs_frequency_curve() on %s (%s)" %
(nml2_file, os.path.abspath(nml2_file)), verbose)
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
traces_ax = None
if_ax = None
iv_ax = None
sim_id = 'iv_%s' % cell_id
total_duration = pre_zero_pulse + analysis_duration + analysis_delay + post_zero_pulse
pulse_duration = analysis_duration + analysis_delay
end_stim = pre_zero_pulse + analysis_duration + analysis_delay
ls = LEMSSimulation(sim_id, total_duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
if len(custom_amps_nA) > 0:
stims = [float(a) for a in custom_amps_nA]
stim_info = ['%snA' % float(a) for a in custom_amps_nA]
else:
amp = start_amp_nA
while amp <= end_amp_nA:
stims.append(amp)
amp += step_nA
stim_info = '(%snA->%snA; %s steps of %snA; %sms)' % (
start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration)
print_comment_v("Generating an IF curve for cell %s in %s using %s %s" %
(cell_id, nml2_file, simulator, stim_info))
number_cells = len(stims)
pop = nml.Population(id="population_of_%s" % cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s" % cell_id
net = nml.Network(id=net_id,
type="networkWithTemperature",
temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA" % stims[i]
input_id = ("input_%s" % stim_amp).replace('.',
'_').replace('-', 'min')
pg = nml.PulseGenerator(id=input_id,
delay="%sms" % pre_zero_pulse,
duration="%sms" % pulse_duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]" % (pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml' % sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0, "Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % sim_id)
for i in range(number_cells):
ref = "v_cell%i" % i
quantity = "%s[%i]/v" % (pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV",
pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
print_comment(
"Written LEMS file %s (%s)" %
(lems_file_name, os.path.abspath(lems_file_name)), verbose)
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NetPyNE":
results = pynml.run_lems_with_jneuroml_netpyne(
lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
num_processors=num_processors,
verbose=verbose)
else:
raise Exception(
"Sorry, cannot yet run current vs frequency analysis using simulator %s"
% simulator)
print_comment(
"Completed run in simulator %s (results: %s)" %
(simulator, results.keys()), verbose)
#print(results.keys())
times_results = []
volts_results = []
volts_labels = []
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t']) * 1000
v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000
if plot_voltage_traces:
times_results.append(t)
volts_results.append(v)
volts_labels.append("%s nA" % stims[i])
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= pre_zero_pulse + analysis_delay and s < (
pre_zero_pulse + analysis_duration + analysis_delay):
count += 1
freq = 1000 * count / float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
#print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
if post_zero_pulse == 0:
iv_results[stims[i]] = v[-1]
else:
v_end = None
for j in range(len(t)):
if v_end == None and t[j] >= end_stim:
v_end = v[j]
iv_results[stims[i]] = v_end
if plot_voltage_traces:
traces_ax = pynml.generate_plot(
times_results,
volts_results,
"Membrane potential traces for: %s" % nml2_file,
xaxis='Time (ms)' if label_xaxis else ' ',
yaxis='Membrane potential (mV)' if label_yaxis else '',
xlim=[total_duration * -0.05, total_duration * 1.05],
show_xticklabels=label_xaxis,
font_size=font_size,
bottom_left_spines_only=bottom_left_spines_only,
grid=False,
labels=volts_labels if show_volts_label else [],
show_plot_already=False,
save_figure_to=save_voltage_traces_to,
title_above_plot=title_above_plot,
verbose=verbose)
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii * 1000 for ii in stims]
freqs = [if_results[s] for s in stims]
if_ax = pynml.generate_plot(
[stims_pA], [freqs],
"Firing frequency versus injected current for: %s" % nml2_file,
colors=[if_iv_color],
linestyles=['-'],
markers=['o'],
linewidths=[linewidth],
xaxis='Input current (pA)' if label_xaxis else ' ',
yaxis='Firing frequency (Hz)' if label_yaxis else '',
xlim=xlim_if,
ylim=ylim_if,
show_xticklabels=label_xaxis,
show_yticklabels=label_yaxis,
font_size=font_size,
bottom_left_spines_only=bottom_left_spines_only,
grid=grid,
show_plot_already=False,
save_figure_to=save_if_figure_to,
title_above_plot=title_above_plot,
verbose=verbose)
if save_if_data_to:
with open(save_if_data_to, 'w') as if_file:
for i in range(len(stims_pA)):
if_file.write("%s\t%s\n" % (stims_pA[i], freqs[i]))
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii * 1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
xs = []
ys = []
xs.append([])
ys.append([])
for si in range(len(stims)):
stim = stims[si]
if len(custom_amps_nA) == 0 and si > 1 and (
stims[si] - stims[si - 1]) > step_nA * 1.01:
xs.append([])
ys.append([])
xs[-1].append(stim * 1000)
ys[-1].append(iv_results[stim])
iv_ax = pynml.generate_plot(
xs,
ys,
"V at %sms versus I below threshold for: %s" %
(end_stim, nml2_file),
colors=[if_iv_color for s in xs],
linestyles=['-' for s in xs],
markers=['o' for s in xs],
xaxis='Input current (pA)' if label_xaxis else '',
yaxis='Membrane potential (mV)' if label_yaxis else '',
xlim=xlim_iv,
ylim=ylim_iv,
show_xticklabels=label_xaxis,
show_yticklabels=label_yaxis,
font_size=font_size,
linewidths=[linewidth for s in xs],
bottom_left_spines_only=bottom_left_spines_only,
grid=grid,
show_plot_already=False,
save_figure_to=save_iv_figure_to,
title_above_plot=title_above_plot,
verbose=verbose)
if save_iv_data_to:
with open(save_iv_data_to, 'w') as iv_file:
for i in range(len(stims_pA)):
iv_file.write("%s\t%s\n" % (stims_pA[i], vs[i]))
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
if return_axes:
return traces_ax, if_ax, iv_ax
return if_results
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
def generate_lems_file_for_neuroml(sim_id,
neuroml_file,
target,
duration,
dt,
lems_file_name,
target_dir,
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
copy_neuroml = True,
seed=None):
if seed:
random.seed(seed) # 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'%(file_name_full,neuroml_file))
ls = LEMSSimulation(sim_id, duration, dt, target)
nml_doc = read_neuroml2_file(neuroml_file, include_includes=True, verbose=True)
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 NeuroML file (%s) to: %s (%s)"%(neuroml_file, target_dir, os.path.abspath(target_dir)))
if os.path.abspath(os.path.dirname(neuroml_file))!=os.path.abspath(target_dir):
shutil.copy(neuroml_file, target_dir)
neuroml_file_name = os.path.basename(neuroml_file)
ls.include_neuroml2_file(neuroml_file_name, include_included=False)
for include in nml_doc.includes:
incl_curr = '%s/%s'%(os.path.dirname(neuroml_file),include.href)
print_comment_v(' - Including %s located at %s'%(include.href, incl_curr))
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
sub_doc = read_neuroml2_file(incl_curr)
for include in sub_doc.includes:
incl_curr = '%s/%s'%(os.path.dirname(neuroml_file),include.href)
print_comment_v(' -- Including %s located at %s'%(include.href, 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 :
for network in nml_doc.networks:
for population in network.populations:
quantity_template = "%s[%i]/v"
component = population.component
size = population.size
cell = None
segment_ids = []
if plot_all_segments:
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+"/v"
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())
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())
if gen_saves_for_all_v or population.id in gen_saves_for_only_populations:
print_comment('Saving %i values of v for %s in population %s'%(size, component, population.id))
of0 = 'Volts_file__%s'%population.id
ls.create_output_file(of0, "%s.%s.v.dat"%(sim_id,population.id))
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)
for display in gen_plots_for_quantities.keys():
quantities = gen_plots_for_quantities[display]
ls.create_display(display, "Plots of %s"%display, "-90", "50")
for q in quantities:
ls.add_line_to_display(display, safe_variable(q), q, "1", get_next_hex_color())
for file_name in gen_saves_for_quantities.keys():
quantities = gen_saves_for_quantities[file_name]
ls.create_output_file(file_name, file_name)
for q in quantities:
ls.add_column_to_output_file(file_name, safe_variable(q), q)
ls.save_to_file(file_name=file_name_full)
return quantities_saved
def generate_lems_channel_analyser(channel_file,
channel,
min_target_voltage,
step_target_voltage,
max_target_voltage,
clamp_delay,
clamp_duration,
clamp_base_voltage,
duration,
erev,
gates,
temperature,
ca_conc,
iv_curve,
scale_dt=1,
dat_suffix='',
verbose=True):
print_comment(("Generating LEMS file to investigate %s in %s, %smV->%smV, "
"%sdegC") % (channel, channel_file, min_target_voltage,
max_target_voltage, temperature), verbose)
target_voltages = []
v = min_target_voltage
while v <= max_target_voltage:
target_voltages.append(v)
v += step_target_voltage
target_voltages_map = []
for t in target_voltages:
fract = float(target_voltages.index(t)) / (len(target_voltages) - 1)
info = {}
info["v"] = t
info["v_str"] = str(t).replace("-", "min")
info["col"] = get_colour_hex(fract)
target_voltages_map.append(info)
includes = get_includes_from_channel_file(channel_file)
includes_relative = []
base_path = os.path.dirname(channel_file)
for inc in includes:
includes_relative.append(os.path.abspath(base_path + '/' + inc))
model = {
"channel_file": channel_file,
"includes": includes_relative,
"channel": channel,
"target_voltages": target_voltages_map,
"clamp_delay": clamp_delay,
"clamp_duration": clamp_duration,
"clamp_base_voltage": clamp_base_voltage,
"min_target_voltage": min_target_voltage,
"max_target_voltage": max_target_voltage,
"duration": duration,
"scale_dt": scale_dt,
"erev": erev,
"gates": gates,
"temperature": temperature,
"ca_conc": ca_conc,
"iv_curve": iv_curve,
"dat_suffix": dat_suffix
}
# pp.pprint(model)
merged = merge_with_template(model, TEMPLATE_FILE)
return merged
def generate_lems_file_for_neuroml(sim_id,
neuroml_file,
target,
duration,
dt,
lems_file_name,
target_dir,
gen_plots_for_all_v = True,
gen_saves_for_all_v = True,
copy_neuroml = True,
seed=None):
if seed:
random.seed(seed) # 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'%(file_name_full,neuroml_file))
ls = LEMSSimulation(sim_id, duration, dt, target)
nml_doc = read_neuroml2_file(neuroml_file)
quantities_saved = []
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:
if os.path.abspath(os.path.dirname(neuroml_file))!=os.path.abspath(target_dir):
shutil.copy(neuroml_file, target_dir)
neuroml_file_name = os.path.basename(neuroml_file)
ls.include_neuroml2_file(neuroml_file_name, include_included=False)
for include in nml_doc.includes:
incl_curr = '%s/%s'%(os.path.dirname(neuroml_file),include.href)
print_comment_v(' - Including %s located at %s'%(include.href, incl_curr))
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
sub_doc = read_neuroml2_file(incl_curr)
for include in sub_doc.includes:
incl_curr = '%s/%s'%(os.path.dirname(neuroml_file),include.href)
print_comment_v(' -- Including %s located at %s'%(include.href, 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:
for network in nml_doc.networks:
for population in network.populations:
size = population.size
component = population.component
quantity_template = "%s[%i]/v"
if population.type and population.type == 'populationList':
quantity_template = "%s/%i/"+component+"/v"
if gen_plots_for_all_v:
print_comment('Generating %i plots for %s in population %s'%(size, component, population.id))
disp0 = 'DispPop__%s'%population.id
ls.create_display(disp0, "Voltages of %s"%disp0, "-90", "50")
for i in range(size):
quantity = quantity_template%(population.id, i)
ls.add_line_to_display(disp0, "v %s"%safe_variable(quantity), quantity, "1mV", get_next_hex_color())
if gen_saves_for_all_v:
print_comment('Saving %i values of v for %s in population %s'%(size, component, population.id))
of0 = 'Volts_file__%s'%population.id
ls.create_output_file(of0, "%s.%s.v.dat"%(sim_id,population.id))
for i in range(size):
quantity = quantity_template%(population.id, i)
ls.add_column_to_output_file(of0, 'v_%s'%safe_variable(quantity), quantity)
quantities_saved.append(quantity)
ls.save_to_file(file_name=file_name_full)
return quantities_saved
def main():
args = process_args()
xmlfile = args.neuroml_file
pov_file_name = (
xmlfile.replace(".xml", ".pov").replace(".nml1", ".pov").replace(".nml.h5", ".pov").replace(".nml", ".pov")
)
pov_file = open(pov_file_name, "w")
header = """
/*
POV-Ray file generated from NeuroML network
*/
#version 3.6;
#include "colors.inc"
background {rgbt %s}
\n""" ### end of header
pov_file.write(header % (args.background))
cells_file = pov_file
net_file = pov_file
splitOut = False
cf = pov_file_name.replace(".pov", "_cells.inc")
nf = pov_file_name.replace(".pov", "_net.inc")
if args.split:
splitOut = True
cells_file = open(cf, "w")
net_file = open(nf, "w")
print_comment_v("Saving into %s and %s and %s" % (pov_file_name, cf, nf))
print_comment_v("Converting XML file: %s to %s" % (xmlfile, pov_file_name))
nml_doc = pynml.read_neuroml2_file(xmlfile, include_includes=True, verbose=args.v)
cell_elements = []
cell_elements.extend(nml_doc.cells)
cell_elements.extend(nml_doc.cell2_ca_poolses)
minXc = 1e9
minYc = 1e9
minZc = 1e9
maxXc = -1e9
maxYc = -1e9
maxZc = -1e9
minX = 1e9
minY = 1e9
minZ = 1e9
maxX = -1e9
maxY = -1e9
maxZ = -1e9
declaredcells = {}
print_comment_v("There are %i cells in the file" % len(cell_elements))
cell_id_vs_seg_id_vs_proximal = {}
cell_id_vs_seg_id_vs_distal = {}
cell_id_vs_cell = {}
for cell in cell_elements:
cellName = cell.id
cell_id_vs_cell[cell.id] = cell
print_comment_v("Handling cell: %s" % cellName)
cell_id_vs_seg_id_vs_proximal[cell.id] = {}
cell_id_vs_seg_id_vs_distal[cell.id] = {}
declaredcell = "cell_" + cellName
declaredcells[cellName] = declaredcell
cells_file.write("#declare %s = \n" % declaredcell)
cells_file.write("union {\n")
prefix = ""
segments = cell.morphology.segments
distpoints = {}
proxpoints = {}
for segment in segments:
id = int(segment.id)
distal = segment.distal
x = float(distal.x)
y = float(distal.y)
z = float(distal.z)
r = max(float(distal.diameter) / 2.0, args.mindiam)
if x - r < minXc:
minXc = x - r
if y - r < minYc:
minYc = y - r
if z - r < minZc:
minZc = z - r
if x + r > maxXc:
maxXc = x + r
if y + r > maxYc:
maxYc = y + r
if z + r > maxZc:
maxZc = z + r
distalpoint = "<%f, %f, %f>, %f " % (x, y, z, r)
distpoints[id] = distalpoint
cell_id_vs_seg_id_vs_distal[cell.id][id] = (x, y, z)
proximalpoint = ""
if segment.proximal is not None:
proximal = segment.proximal
proximalpoint = "<%f, %f, %f>, %f " % (
float(proximal.x),
float(proximal.y),
float(proximal.z),
max(float(proximal.diameter) / 2.0, args.mindiam),
)
cell_id_vs_seg_id_vs_proximal[cell.id][id] = (float(proximal.x), float(proximal.y), float(proximal.z))
else:
parent = int(segment.parent.segments)
proximalpoint = distpoints[parent]
cell_id_vs_seg_id_vs_proximal[cell.id][id] = cell_id_vs_seg_id_vs_distal[cell.id][parent]
proxpoints[id] = proximalpoint
shape = "cone"
if proximalpoint == distalpoint:
shape = "sphere"
proximalpoint = ""
if shape == "cone" and (proximalpoint.split(">")[0] == distalpoint.split(">")[0]):
comment = "Ignoring zero length segment (id = %i): %s -> %s\n" % (id, proximalpoint, distalpoint)
print_comment_v(comment)
cells_file.write(" // " + comment)
else:
cells_file.write(" %s {\n" % shape)
cells_file.write(" %s\n" % distalpoint)
if len(proximalpoint):
cells_file.write(" %s\n" % proximalpoint)
cells_file.write(" //%s_%s.%s\n" % ("CELL_GROUP_NAME", "0", id))
cells_file.write(" }\n")
cells_file.write(" pigment { color rgb <%f,%f,%f> }\n" % (random.random(), random.random(), random.random()))
cells_file.write("}\n\n")
if splitOut:
pov_file.write('#include "' + cf + '"\n\n')
pov_file.write('#include "' + nf + '"\n\n')
pov_file.write(
"""\n/*\n Defining a dummy cell to use when cell in population is not found in NeuroML file...\n*/\n#declare %s =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 5.000000
}
pigment { color rgb <1,0,0> }
}\n"""
% _DUMMY_CELL
)
pov_file.write(
"""\n/*\n Defining the spheres to use for end points of connections...\n*/\n#declare conn_start_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <0,1,0> }
}\n\n#declare conn_end_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <1,0,0> }
}\n"""
)
positions = {}
popElements = nml_doc.networks[0].populations
pop_id_vs_cell = {}
print_comment_v("There are %i populations in the file" % len(popElements))
for pop in popElements:
name = pop.id
celltype = pop.component
instances = pop.instances
if cell_id_vs_cell.has_key(pop.component):
pop_id_vs_cell[pop.id] = cell_id_vs_cell[pop.component]
info = "Population: %s has %i positioned cells of type: %s" % (name, len(instances), celltype)
print_comment_v(info)
colour = "1"
for prop in pop.properties:
if prop.tag == "color":
colour = prop.value
colour = colour.replace(" ", ",")
# print "Colour determined to be: "+colour
net_file.write("\n\n/* " + info + " */\n\n")
pop_positions = {}
if not celltype in declaredcells:
cell_definition = _DUMMY_CELL
minXc = 0
minYc = 0
minZc = 0
maxXc = 0
maxYc = 0
maxZc = 0
else:
cell_definition = declaredcells[celltype]
for instance in instances:
location = instance.location
id = int(instance.id)
net_file.write("object {\n")
net_file.write(" %s\n" % cell_definition)
x = float(location.x)
y = float(location.y)
z = float(location.z)
pop_positions[id] = (x, y, z)
if x + minXc < minX:
minX = x + minXc
if y + minYc < minY:
minY = y + minYc
if z + minZc < minZ:
minZ = z + minZc
if x + maxXc > maxX:
maxX = x + maxXc
if y + maxYc > maxY:
maxY = y + maxYc
if z + maxZc > maxZ:
maxZ = z + maxZc
net_file.write(" translate <%s, %s, %s>\n" % (x, y, z))
if colour == "1":
colour = "%f,%f,%f" % (random.random(), random.random(), random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }" % (colour))
net_file.write("\n //%s_%s\n" % (name, id))
net_file.write("}\n")
positions[name] = pop_positions
if len(instances) == 0 and int(pop.size > 0):
info = "Population: %s has %i unpositioned cells of type: %s" % (name, pop.size, celltype)
print_comment_v(info)
colour = "1"
"""
if pop.annotation:
print dir(pop.annotation)
print pop.annotation.anytypeobjs_
print pop.annotation.member_data_items_[0].name
print dir(pop.annotation.member_data_items_[0])
for prop in pop.annotation.anytypeobjs_:
print prop
if len(prop.getElementsByTagName('meta:tag'))>0 and prop.getElementsByTagName('meta:tag')[0].childNodes[0].data == 'color':
#print prop.getElementsByTagName('meta:tag')[0].childNodes
colour = prop.getElementsByTagName('meta:value')[0].childNodes[0].data
colour = colour.replace(" ", ",")
elif prop.hasAttribute('tag') and prop.getAttribute('tag') == 'color':
colour = prop.getAttribute('value')
colour = colour.replace(" ", ",")
print "Colour determined to be: "+colour
"""
net_file.write("\n\n/* " + info + " */\n\n")
net_file.write("object {\n")
net_file.write(" %s\n" % cell_definition)
x = 0
y = 0
z = 0
if x + minXc < minX:
minX = x + minXc
if y + minYc < minY:
minY = y + minYc
if z + minZc < minZ:
minZ = z + minZc
if x + maxXc > maxX:
maxX = x + maxXc
if y + maxYc > maxY:
maxY = y + maxYc
if z + maxZc > maxZ:
maxZ = z + maxZc
net_file.write(" translate <%s, %s, %s>\n" % (x, y, z))
if colour == "1":
colour = "%f,%f,%f" % (random.random(), random.random(), random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }" % (colour))
net_file.write("\n //%s_%s\n" % (name, id))
net_file.write("}\n")
# print positions
if (
args.conns or args.conn_points
): # Note: segment specific connections not implemented yet... i.e. connections from dends to axons...
# print_comment_v("************************\n*\n* Note: connection lines in 3D do not yet target dendritic locations!\n*\n************************")
for projection in nml_doc.networks[0].projections:
pre = projection.presynaptic_population
post = projection.postsynaptic_population
connections = projection.connections + projection.connection_wds
print_comment_v("Adding %i connections %s -> %s " % (len(connections), pre, post))
# print cell_id_vs_seg_id_vs_distal
# print cell_id_vs_seg_id_vs_proximal
for connection in connections:
pre_cell_id = connection.get_pre_cell_id()
post_cell_id = connection.get_post_cell_id()
pre_loc = (0, 0, 0)
if positions.has_key(pre):
if len(positions[pre]) > 0:
pre_loc = positions[pre][pre_cell_id]
post_loc = (0, 0, 0)
if positions.has_key(post):
post_loc = positions[post][post_cell_id]
if pop_id_vs_cell.has_key(projection.presynaptic_population):
pre_cell = pop_id_vs_cell[projection.presynaptic_population]
d = cell_id_vs_seg_id_vs_distal[pre_cell.id][int(connection.pre_segment_id)]
p = cell_id_vs_seg_id_vs_proximal[pre_cell.id][int(connection.pre_segment_id)]
m = [p[i] + float(connection.pre_fraction_along) * (d[i] - p[i]) for i in [0, 1, 2]]
print_comment("Pre point is %s, %s between %s and %s" % (m, connection.pre_fraction_along, p, d))
pre_loc = [pre_loc[i] + m[i] for i in [0, 1, 2]]
if pop_id_vs_cell.has_key(projection.postsynaptic_population):
post_cell = pop_id_vs_cell[projection.postsynaptic_population]
d = cell_id_vs_seg_id_vs_distal[post_cell.id][int(connection.post_segment_id)]
p = cell_id_vs_seg_id_vs_proximal[post_cell.id][int(connection.post_segment_id)]
m = [p[i] + float(connection.post_fraction_along) * (d[i] - p[i]) for i in [0, 1, 2]]
print_comment("Post point is %s, %s between %s and %s" % (m, connection.post_fraction_along, p, d))
post_loc = [post_loc[i] + m[i] for i in [0, 1, 2]]
if post_loc != pre_loc:
info = "// Connection from %s:%s %s -> %s:%s %s\n" % (
pre,
pre_cell_id,
pre_loc,
post,
post_cell_id,
post_loc,
)
print_comment(info)
net_file.write("// %s" % info)
if args.conns:
net_file.write(
"cylinder { <%s,%s,%s>, <%s,%s,%s>, .5 pigment{color Grey}}\n"
% (pre_loc[0], pre_loc[1], pre_loc[2], post_loc[0], post_loc[1], post_loc[2])
)
if args.conn_points:
net_file.write(
"object { conn_start_point translate <%s,%s,%s> }\n" % (pre_loc[0], pre_loc[1], pre_loc[2])
)
net_file.write(
"object { conn_end_point translate <%s,%s,%s> }\n" % (post_loc[0], post_loc[1], post_loc[2])
)
plane = """
plane {
y, vv(-1)
pigment {checker color rgb 1.0, color rgb 0.8 scale 20}
}
"""
footer = """
#declare minX = %f;
#declare minY = %f;
#declare minZ = %f;
#declare maxX = %f;
#declare maxY = %f;
#declare maxZ = %f;
#macro uu(xx)
0.5 * (maxX *(1+xx) + minX*(1-xx))
#end
#macro vv(xx)
0.5 * (maxY *(1+xx) + minY*(1-xx))
#end
#macro ww(xx)
0.5 * (maxZ *(1+xx) + minZ*(1-xx))
#end
light_source {
<uu(5),uu(2),uu(5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(5),uu(-2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(-2),uu(5)>
color rgb <1,1,1>
}
// Trying to view box
camera {
location < uu(%s + %s * sin (clock * 2 * 3.141)) , vv(%s + %s * sin (clock * 2 * 3.141)) , ww(%s + %s * cos (clock * 2 * 3.141)) >
look_at < uu(%s + 0) , vv(%s + 0.05+0.3*sin (clock * 2 * 3.141)) , ww(%s + 0)>
}
%s
\n""" % (
minX,
minY,
minZ,
maxX,
maxY,
maxZ,
args.posx,
args.scalex,
args.posy,
args.scaley,
args.posz,
args.scalez,
args.viewx,
args.viewy,
args.viewz,
(plane if args.plane else ""),
) ### end of footer
pov_file.write(footer)
pov_file.close()
if args.movie:
ini_file_name = pov_file_name.replace(".pov", "_movie.ini")
ini_movie = """
Antialias=On
+W800 +H600
Antialias_Threshold=0.3
Antialias_Depth=4
Input_File_Name=%s
Initial_Frame=1
Final_Frame=%i
Initial_Clock=0
Final_Clock=1
Cyclic_Animation=on
Pause_when_Done=off
"""
ini_file = open(ini_file_name, "w")
ini_file.write(ini_movie % (pov_file_name, args.frames))
ini_file.close()
print_comment_v(
"Created file for generating %i movie frames at: %s. To run this type:\n\n povray %s\n"
% (args.frames, ini_file_name, ini_file_name)
)
else:
print_comment_v(
"Created file for generating image of network. To run this type:\n\n povray %s\n" % (pov_file_name)
)
print_comment_v(
"Or for higher resolution:\n\n povray Antialias=On Antialias_Depth=10 Antialias_Threshold=0.1 +W1200 +H900 %s\n"
% (pov_file_name)
)
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA = -0.1,
end_amp_nA = 0.1,
step_nA = 0.01,
custom_amps_nA = [],
analysis_duration = 1000,
analysis_delay = 0,
pre_zero_pulse = 0,
post_zero_pulse = 0,
dt = 0.05,
temperature = "32degC",
spike_threshold_mV = 0.,
plot_voltage_traces = False,
plot_if = True,
plot_iv = False,
xlim_if = None,
ylim_if = None,
xlim_iv = None,
ylim_iv = None,
label_xaxis = True,
label_yaxis = True,
show_volts_label = True,
grid = True,
font_size = 12,
if_iv_color = 'k',
linewidth = 1,
bottom_left_spines_only = False,
show_plot_already = True,
save_voltage_traces_to = None,
save_if_figure_to = None,
save_iv_figure_to = None,
save_if_data_to = None,
save_iv_data_to = None,
simulator = "jNeuroML",
num_processors = 1,
include_included = True,
title_above_plot = False,
return_axes = False,
verbose = False):
print_comment("Running generate_current_vs_frequency_curve() on %s (%s)"%(nml2_file,os.path.abspath(nml2_file)), verbose)
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
traces_ax = None
if_ax = None
iv_ax = None
sim_id = 'iv_%s'%cell_id
total_duration = pre_zero_pulse+analysis_duration+analysis_delay+post_zero_pulse
pulse_duration = analysis_duration+analysis_delay
end_stim = pre_zero_pulse+analysis_duration+analysis_delay
ls = LEMSSimulation(sim_id, total_duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
if len(custom_amps_nA)>0:
stims = [float(a) for a in custom_amps_nA]
stim_info = ['%snA'%float(a) for a in custom_amps_nA]
else:
amp = start_amp_nA
while amp<=end_amp_nA :
stims.append(amp)
amp+=step_nA
stim_info = '(%snA->%snA; %s steps of %snA; %sms)'%(start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration)
print_comment_v("Generating an IF curve for cell %s in %s using %s %s"%
(cell_id, nml2_file, simulator, stim_info))
number_cells = len(stims)
pop = nml.Population(id="population_of_%s"%cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s"%cell_id
net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA"%stims[i]
input_id = ("input_%s"%stim_amp).replace('.','_').replace('-','min')
pg = nml.PulseGenerator(id=input_id,
delay="%sms"%pre_zero_pulse,
duration="%sms"%pulse_duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]"%(pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml'%sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0,"Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
for i in range(number_cells):
ref = "v_cell%i"%i
quantity = "%s[%i]/v"%(pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
print_comment("Written LEMS file %s (%s)"%(lems_file_name,os.path.abspath(lems_file_name)), verbose)
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NetPyNE":
results = pynml.run_lems_with_jneuroml_netpyne(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
num_processors = num_processors,
verbose=verbose)
else:
raise Exception("Sorry, cannot yet run current vs frequency analysis using simulator %s"%simulator)
print_comment("Completed run in simulator %s (results: %s)"%(simulator,results.keys()), verbose)
#print(results.keys())
times_results = []
volts_results = []
volts_labels = []
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t'])*1000
v = np.array(results["%s[%i]/v"%(pop.id, i)])*1000
if plot_voltage_traces:
times_results.append(t)
volts_results.append(v)
volts_labels.append("%s nA"%stims[i])
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= pre_zero_pulse + analysis_delay and s < (pre_zero_pulse + analysis_duration+analysis_delay):
count+=1
freq = 1000 * count/float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
#print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
if post_zero_pulse==0:
iv_results[stims[i]] = v[-1]
else:
v_end = None
for j in range(len(t)):
if v_end==None and t[j]>=end_stim:
v_end = v[j]
iv_results[stims[i]] = v_end
if plot_voltage_traces:
traces_ax = pynml.generate_plot(times_results,
volts_results,
"Membrane potential traces for: %s"%nml2_file,
xaxis = 'Time (ms)' if label_xaxis else ' ',
yaxis = 'Membrane potential (mV)' if label_yaxis else '',
xlim = [total_duration*-0.05,total_duration*1.05],
show_xticklabels = label_xaxis,
font_size = font_size,
bottom_left_spines_only = bottom_left_spines_only,
grid = False,
labels = volts_labels if show_volts_label else [],
show_plot_already=False,
save_figure_to = save_voltage_traces_to,
title_above_plot = title_above_plot,
verbose=verbose)
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii*1000 for ii in stims]
freqs = [if_results[s] for s in stims]
if_ax = pynml.generate_plot([stims_pA],
[freqs],
"Firing frequency versus injected current for: %s"%nml2_file,
colors = [if_iv_color],
linestyles=['-'],
markers=['o'],
linewidths = [linewidth],
xaxis = 'Input current (pA)' if label_xaxis else ' ',
yaxis = 'Firing frequency (Hz)' if label_yaxis else '',
xlim = xlim_if,
ylim = ylim_if,
show_xticklabels = label_xaxis,
show_yticklabels = label_yaxis,
font_size = font_size,
bottom_left_spines_only = bottom_left_spines_only,
grid = grid,
show_plot_already=False,
save_figure_to = save_if_figure_to,
title_above_plot = title_above_plot,
verbose=verbose)
if save_if_data_to:
with open(save_if_data_to,'w') as if_file:
for i in range(len(stims_pA)):
if_file.write("%s\t%s\n"%(stims_pA[i],freqs[i]))
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii*1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
xs = []
ys = []
xs.append([])
ys.append([])
for si in range(len(stims)):
stim = stims[si]
if len(custom_amps_nA)==0 and si>1 and (stims[si]-stims[si-1])>step_nA*1.01:
xs.append([])
ys.append([])
xs[-1].append(stim*1000)
ys[-1].append(iv_results[stim])
iv_ax = pynml.generate_plot(xs,
ys,
"V at %sms versus I below threshold for: %s"%(end_stim,nml2_file),
colors = [if_iv_color for s in xs],
linestyles=['-' for s in xs],
markers=['o' for s in xs],
xaxis = 'Input current (pA)' if label_xaxis else '',
yaxis = 'Membrane potential (mV)' if label_yaxis else '',
xlim = xlim_iv,
ylim = ylim_iv,
show_xticklabels = label_xaxis,
show_yticklabels = label_yaxis,
font_size = font_size,
linewidths = [linewidth for s in xs],
bottom_left_spines_only = bottom_left_spines_only,
grid = grid,
show_plot_already=False,
save_figure_to = save_iv_figure_to,
title_above_plot = title_above_plot,
verbose=verbose)
if save_iv_data_to:
with open(save_iv_data_to,'w') as iv_file:
for i in range(len(stims_pA)):
iv_file.write("%s\t%s\n"%(stims_pA[i],vs[i]))
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
if return_axes:
return traces_ax, if_ax, iv_ax
return if_results
def main ():
args = process_args()
xmlfile = args.neuroml_file
pov_file_name = xmlfile
endings = [".xml",".h5",".nml"]
for e in endings:
if pov_file_name.endswith(e):
pov_file_name.replace(e, ".pov")
if pov_file_name == xmlfile:
pov_file_name+='.pov'
pov_file = open(pov_file_name, "w")
header='''
/*
POV-Ray file generated from NeuroML network
*/
#version 3.6;
#include "colors.inc"
background {rgbt %s}
\n''' ### end of header
pov_file.write(header%(args.background))
cells_file = pov_file
net_file = pov_file
splitOut = False
cf = pov_file_name.replace(".pov", "_cells.inc")
nf = pov_file_name.replace(".pov", "_net.inc")
if args.split:
splitOut = True
cells_file = open(cf, "w")
net_file = open(nf, "w")
print_comment_v("Saving into %s and %s and %s"%(pov_file_name, cf, nf))
print_comment_v("Converting XML file: %s to %s"%(xmlfile, pov_file_name))
nml_doc = pynml.read_neuroml2_file(xmlfile, include_includes=True, verbose=args.v, optimized=True)
cell_elements = []
cell_elements.extend(nml_doc.cells)
cell_elements.extend(nml_doc.cell2_ca_poolses)
minXc = 1e9
minYc = 1e9
minZc = 1e9
maxXc = -1e9
maxYc = -1e9
maxZc = -1e9
minX = 1e9
minY = 1e9
minZ = 1e9
maxX = -1e9
maxY = -1e9
maxZ = -1e9
declaredcells = {}
print_comment_v("There are %i cells in the file"%len(cell_elements))
cell_id_vs_seg_id_vs_proximal = {}
cell_id_vs_seg_id_vs_distal = {}
cell_id_vs_cell = {}
for cell in cell_elements:
cellName = cell.id
cell_id_vs_cell[cell.id] = cell
print_comment_v("Handling cell: %s"%cellName)
cell_id_vs_seg_id_vs_proximal[cell.id] = {}
cell_id_vs_seg_id_vs_distal[cell.id] = {}
declaredcell = "cell_"+cellName
declaredcells[cellName] = declaredcell
cells_file.write("#declare %s = \n"%declaredcell)
cells_file.write("union {\n")
prefix = ""
segments = cell.morphology.segments
distpoints = {}
proxpoints = {}
for segment in segments:
id = int(segment.id)
distal = segment.distal
x = float(distal.x)
y = float(distal.y)
z = float(distal.z)
r = max(float(distal.diameter)/2.0, args.mindiam)
if x-r<minXc: minXc=x-r
if y-r<minYc: minYc=y-r
if z-r<minZc: minZc=z-r
if x+r>maxXc: maxXc=x+r
if y+r>maxYc: maxYc=y+r
if z+r>maxZc: maxZc=z+r
distalpoint = "<%f, %f, %f>, %f "%(x,y,z,r)
distpoints[id] = distalpoint
cell_id_vs_seg_id_vs_distal[cell.id][id] = (x,y,z)
proximalpoint = ""
if segment.proximal is not None:
proximal = segment.proximal
proximalpoint = "<%f, %f, %f>, %f "%(float(proximal.x),float(proximal.y),float(proximal.z),max(float(proximal.diameter)/2.0, args.mindiam))
cell_id_vs_seg_id_vs_proximal[cell.id][id] = (float(proximal.x),float(proximal.y),float(proximal.z))
else:
parent = int(segment.parent.segments)
proximalpoint = distpoints[parent]
cell_id_vs_seg_id_vs_proximal[cell.id][id] = cell_id_vs_seg_id_vs_distal[cell.id][parent]
proxpoints[id] = proximalpoint
shape = "cone"
if proximalpoint == distalpoint:
shape = "sphere"
proximalpoint = ""
if ( shape == "cone" and (proximalpoint.split('>')[0] == distalpoint.split('>')[0])):
comment = "Ignoring zero length segment (id = %i): %s -> %s\n"%(id, proximalpoint, distalpoint)
print_comment_v(comment)
cells_file.write(" // "+comment)
else:
cells_file.write(" %s {\n"%shape)
cells_file.write(" %s\n"%distalpoint)
if len(proximalpoint): cells_file.write(" %s\n"%proximalpoint)
cells_file.write(" //%s_%s.%s\n"%('CELL_GROUP_NAME','0', id))
cells_file.write(" }\n")
if args.segids:
cells_file.write(' text {\n')
cells_file.write(' ttf "timrom.ttf" "------- Segment: %s" .1, 0.01\n'%(segment.id))
cells_file.write(' pigment { Red }\n')
cells_file.write(' rotate <0,180,0>\n')
cells_file.write(' scale <10,10,10>')
cells_file.write(' translate %s>\n'%distalpoint.split('>')[0])
cells_file.write(' }\n')
cells_file.write(" pigment { color rgb <%f,%f,%f> }\n"%(random.random(),random.random(),random.random()))
cells_file.write("}\n\n")
if splitOut:
pov_file.write("#include \""+cf+"\"\n\n")
pov_file.write("#include \""+nf+"\"\n\n")
pov_file.write('''\n/*\n Defining a dummy cell to use when cell in population is not found in NeuroML file...\n*/\n#declare %s =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 5.000000
}
pigment { color rgb <1,0,0> }
}\n'''%_DUMMY_CELL)
pov_file.write('''\n/*\n Defining the spheres to use for end points of connections...\n*/
\n#declare conn_start_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <0,1,0> }
}\n
\n#declare conn_end_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <1,0,0> }
}\n
\n#declare input_object =
union {
cone {
<0, 0, 0>, 0.1 // Center and radius of one end
<0, -40, 0>, 2.5 // Center and radius of other end
}
pigment { color rgb <0.2,0.2,0.8> }
}\n''')
positions = {}
popElements = nml_doc.networks[0].populations
pop_id_vs_cell = {}
print_comment_v("There are %i populations in the file"%len(popElements))
for pop in popElements:
name = pop.id
celltype = pop.component
instances = pop.instances
if pop.component in cell_id_vs_cell.keys():
pop_id_vs_cell[pop.id] = cell_id_vs_cell[pop.component]
info = "Population: %s has %i positioned cells of type: %s"%(name,len(instances),celltype)
print_comment_v(info)
colour = "1"
substitute_radius = None
for prop in pop.properties:
if prop.tag == 'color':
colour = prop.value
colour = colour.replace(" ", ",")
#print "Colour determined to be: "+colour
if prop.tag == 'radius':
substitute_radius = float(prop.value)
net_file.write("\n\n/* "+info+" */\n\n")
pop_positions = {}
if not celltype in declaredcells:
minXc = 0
minYc = 0
minZc = 0
maxXc = 0
maxYc = 0
maxZc = 0
if substitute_radius:
dummy_cell_name = define_dummy_cell(name, substitute_radius, pov_file)
cell_definition = dummy_cell_name
else:
cell_definition = _DUMMY_CELL
else:
cell_definition = declaredcells[celltype]
for instance in instances:
location = instance.location
id = int(instance.id)
net_file.write("object {\n")
net_file.write(" %s\n"%cell_definition)
x = float(location.x)
y = float(location.y)
z = float(location.z)
pop_positions[id] = (x,y,z)
if x+minXc<minX: minX=x+minXc
if y+minYc<minY: minY=y+minYc
if z+minZc<minZ: minZ=z+minZc
if x+maxXc>maxX: maxX=x+maxXc
if y+maxYc>maxY: maxY=y+maxYc
if z+maxZc>maxZ: maxZ=z+maxZc
net_file.write(" translate <%s, %s, %s>\n"%(x,y,z))
if colour == '1':
colour = "%f,%f,%f"%(random.random(),random.random(),random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }"%(colour))
net_file.write("\n //%s_%s\n"%(name, id))
net_file.write("}\n")
positions[name] = pop_positions
if len(instances) == 0 and int(pop.size>0):
info = "Population: %s has %i unpositioned cells of type: %s"%(name,pop.size,celltype)
print_comment_v(info)
colour = "1"
'''
if pop.annotation:
print dir(pop.annotation)
print pop.annotation.anytypeobjs_
print pop.annotation.member_data_items_[0].name
print dir(pop.annotation.member_data_items_[0])
for prop in pop.annotation.anytypeobjs_:
print prop
if len(prop.getElementsByTagName('meta:tag'))>0 and prop.getElementsByTagName('meta:tag')[0].childNodes[0].data == 'color':
#print prop.getElementsByTagName('meta:tag')[0].childNodes
colour = prop.getElementsByTagName('meta:value')[0].childNodes[0].data
colour = colour.replace(" ", ",")
elif prop.hasAttribute('tag') and prop.getAttribute('tag') == 'color':
colour = prop.getAttribute('value')
colour = colour.replace(" ", ",")
print "Colour determined to be: "+colour
'''
net_file.write("\n\n/* "+info+" */\n\n")
net_file.write("object {\n")
net_file.write(" %s\n"%cell_definition)
x = 0
y = 0
z = 0
if x+minXc<minX: minX=x+minXc
if y+minYc<minY: minY=y+minYc
if z+minZc<minZ: minZ=z+minZc
if x+maxXc>maxX: maxX=x+maxXc
if y+maxYc>maxY: maxY=y+maxYc
if z+maxZc>maxZ: maxZ=z+maxZc
net_file.write(" translate <%s, %s, %s>\n"%(x,y,z))
if colour == '1':
colour = "%f,%f,%f"%(random.random(),random.random(),random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }"%(colour))
net_file.write("\n //%s_%s\n"%(name, id))
net_file.write("}\n")
if args.conns or args.conn_points:
projections = nml_doc.networks[0].projections + nml_doc.networks[0].electrical_projections + nml_doc.networks[0].continuous_projections
for projection in projections:
pre = projection.presynaptic_population
post = projection.postsynaptic_population
if isinstance(projection, neuroml.Projection):
connections = []
for c in projection.connection_wds: connections.append(c)
for c in projection.connections: connections.append(c)
color='Grey'
elif isinstance(projection, neuroml.ElectricalProjection):
connections = projection.electrical_connections + projection.electrical_connection_instances + projection.electrical_connection_instance_ws
color='Yellow'
elif isinstance(projection, neuroml.ContinuousProjection):
connections = projection.continuous_connections + projection.continuous_connection_instances + projection.continuous_connection_instance_ws
color='Blue'
print_comment_v("Adding %i connections for %s: %s -> %s "%(len(connections),projection.id,pre,post))
#print cell_id_vs_seg_id_vs_distal
#print cell_id_vs_seg_id_vs_proximal
for connection in connections:
pre_cell_id = connection.get_pre_cell_id()
post_cell_id = connection.get_post_cell_id()
pre_loc = (0,0,0)
if pre in positions.keys():
if len(positions[pre])>0:
pre_loc = positions[pre][pre_cell_id]
post_loc = (0,0,0)
if post in positions.keys():
post_loc = positions[post][post_cell_id]
if projection.presynaptic_population in pop_id_vs_cell.keys():
pre_cell = pop_id_vs_cell[projection.presynaptic_population]
d = cell_id_vs_seg_id_vs_distal[pre_cell.id][connection.get_pre_segment_id()]
p = cell_id_vs_seg_id_vs_proximal[pre_cell.id][connection.get_pre_segment_id()]
m = [ p[i]+connection.get_pre_fraction_along()*(d[i]-p[i]) for i in [0,1,2] ]
print_comment("Pre point is %s, %s between %s and %s"%(m,connection.get_pre_fraction_along(),p,d))
pre_loc = [ pre_loc[i]+m[i] for i in [0,1,2] ]
if projection.postsynaptic_population in pop_id_vs_cell.keys():
post_cell = pop_id_vs_cell[projection.postsynaptic_population]
d = cell_id_vs_seg_id_vs_distal[post_cell.id][connection.get_post_segment_id()]
p = cell_id_vs_seg_id_vs_proximal[post_cell.id][connection.get_post_segment_id()]
m = [ p[i]+connection.get_post_fraction_along()*(d[i]-p[i]) for i in [0,1,2] ]
print_comment("Post point is %s, %s between %s and %s"%(m,connection.get_post_fraction_along(),p,d))
post_loc = [ post_loc[i]+m[i] for i in [0,1,2] ]
if post_loc != pre_loc:
info = "// Connection from %s:%s %s -> %s:%s %s\n"%(pre, pre_cell_id, pre_loc, post, post_cell_id, post_loc)
print_comment(info)
net_file.write("// %s"%info)
if args.conns:
net_file.write("cylinder { <%s,%s,%s>, <%s,%s,%s>, .5 pigment{color %s}}\n"%(pre_loc[0],pre_loc[1],pre_loc[2], post_loc[0],post_loc[1],post_loc[2],color))
if args.conn_points:
net_file.write("object { conn_start_point translate <%s,%s,%s> }\n"%(pre_loc[0],pre_loc[1],pre_loc[2]))
net_file.write("object { conn_end_point translate <%s,%s,%s> }\n"%(post_loc[0],post_loc[1],post_loc[2]))
if args.inputs:
for il in nml_doc.networks[0].input_lists:
for input in il.input:
popi = il.populations
cell_id = input.get_target_cell_id()
cell = pop_id_vs_cell[popi]
loc = (0,0,0)
if popi in positions.keys():
if len(positions[popi])>0:
loc = positions[popi][cell_id]
d = cell_id_vs_seg_id_vs_distal[cell.id][input.get_segment_id()]
p = cell_id_vs_seg_id_vs_proximal[cell.id][input.get_segment_id()]
m = [ p[i]+input.get_fraction_along()*(d[i]-p[i]) for i in [0,1,2] ]
input_info = "Input on cell %s:%s at %s; point %s along (%s -> %s): %s"%(popi,cell_id, loc,input.get_fraction_along(),d,p,m)
loc = [ loc[i]+m[i] for i in [0,1,2] ]
net_file.write("/* %s */\n"%input_info)
net_file.write("object { input_object translate <%s,%s,%s> }\n\n"%(loc[0],loc[1],loc[2]))
plane = '''
plane {
y, vv(-1)
pigment {checker color rgb 1.0, color rgb 0.8 scale 20}
}
'''
footer='''
#declare minX = %f;
#declare minY = %f;
#declare minZ = %f;
#declare maxX = %f;
#declare maxY = %f;
#declare maxZ = %f;
#macro uu(xx)
0.5 * (maxX *(1+xx) + minX*(1-xx))
#end
#macro vv(xx)
0.5 * (maxY *(1+xx) + minY*(1-xx))
#end
#macro ww(xx)
0.5 * (maxZ *(1+xx) + minZ*(1-xx))
#end
light_source {
<uu(5),uu(2),uu(5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(5),uu(-2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(-2),uu(5)>
color rgb <1,1,1>
}
// Trying to view box
camera {
location < uu(%s + %s * sin (clock * 2 * 3.141)) , vv(%s + %s * sin (clock * 2 * 3.141)) , ww(%s + %s * cos (clock * 2 * 3.141)) >
look_at < uu(%s + 0) , vv(%s + 0.05+0.3*sin (clock * 2 * 3.141)) , ww(%s + 0)>
}
%s
\n'''%(minX,minY,minZ,maxX,maxY,maxZ, args.posx, args.scalex, args.posy, args.scaley, args.posz, args.scalez, args.viewx, args.viewy, args.viewz, (plane if args.plane else "")) ### end of footer
pov_file.write(footer)
pov_file.close()
if args.movie:
ini_file_name = pov_file_name.replace(".pov", "_movie.ini")
ini_movie = '''
Antialias=On
+W800 +H600
Antialias_Threshold=0.3
Antialias_Depth=4
Input_File_Name=%s
Initial_Frame=1
Final_Frame=%i
Initial_Clock=0
Final_Clock=1
Cyclic_Animation=on
Pause_when_Done=off
'''
ini_file = open(ini_file_name, 'w')
ini_file.write(ini_movie%(pov_file_name, args.frames))
ini_file.close()
print_comment_v("Created file for generating %i movie frames at: %s. To run this type:\n\n povray %s\n"%(args.frames,ini_file_name,ini_file_name))
else:
print_comment_v("Created file for generating image of network. To run this type:\n\n povray %s\n"%(pov_file_name))
print_comment_v("Or for higher resolution:\n\n povray Antialias=On Antialias_Depth=10 Antialias_Threshold=0.1 +W1200 +H900 %s\n"%(pov_file_name))
def main ():
args = process_args()
xmlfile = args.neuroml_file
pov_file_name = xmlfile.replace(".xml", ".pov").replace(".nml1", ".pov").replace(".nml.h5", ".pov").replace(".nml", ".pov")
pov_file = open(pov_file_name, "w")
header='''
/*
POV-Ray file generated from NeuroML network
*/
#version 3.6;
#include "colors.inc"
background {rgbt %s}
\n''' ### end of header
pov_file.write(header%(args.background))
cells_file = pov_file
net_file = pov_file
splitOut = False
cf = pov_file_name.replace(".pov", "_cells.inc")
nf = pov_file_name.replace(".pov", "_net.inc")
if args.split:
splitOut = True
cells_file = open(cf, "w")
net_file = open(nf, "w")
print_comment_v("Saving into %s and %s and %s"%(pov_file_name, cf, nf))
print_comment_v("Converting XML file: %s to %s"%(xmlfile, pov_file_name))
nml_doc = pynml.read_neuroml2_file(xmlfile, include_includes=True, verbose=args.v)
cell_elements = []
cell_elements.extend(nml_doc.cells)
cell_elements.extend(nml_doc.cell2_ca_poolses)
minXc = 1e9
minYc = 1e9
minZc = 1e9
maxXc = -1e9
maxYc = -1e9
maxZc = -1e9
minX = 1e9
minY = 1e9
minZ = 1e9
maxX = -1e9
maxY = -1e9
maxZ = -1e9
declaredcells = {}
print_comment_v("There are %i cells in the file"%len(cell_elements))
cell_id_vs_seg_id_vs_proximal = {}
cell_id_vs_seg_id_vs_distal = {}
cell_id_vs_cell = {}
for cell in cell_elements:
cellName = cell.id
cell_id_vs_cell[cell.id] = cell
print_comment_v("Handling cell: %s"%cellName)
cell_id_vs_seg_id_vs_proximal[cell.id] = {}
cell_id_vs_seg_id_vs_distal[cell.id] = {}
declaredcell = "cell_"+cellName
declaredcells[cellName] = declaredcell
cells_file.write("#declare %s = \n"%declaredcell)
cells_file.write("union {\n")
prefix = ""
segments = cell.morphology.segments
distpoints = {}
proxpoints = {}
for segment in segments:
id = int(segment.id)
distal = segment.distal
x = float(distal.x)
y = float(distal.y)
z = float(distal.z)
r = max(float(distal.diameter)/2.0, args.mindiam)
if x-r<minXc: minXc=x-r
if y-r<minYc: minYc=y-r
if z-r<minZc: minZc=z-r
if x+r>maxXc: maxXc=x+r
if y+r>maxYc: maxYc=y+r
if z+r>maxZc: maxZc=z+r
distalpoint = "<%f, %f, %f>, %f "%(x,y,z,r)
distpoints[id] = distalpoint
cell_id_vs_seg_id_vs_distal[cell.id][id] = (x,y,z)
proximalpoint = ""
if segment.proximal is not None:
proximal = segment.proximal
proximalpoint = "<%f, %f, %f>, %f "%(float(proximal.x),float(proximal.y),float(proximal.z),max(float(proximal.diameter)/2.0, args.mindiam))
cell_id_vs_seg_id_vs_proximal[cell.id][id] = (float(proximal.x),float(proximal.y),float(proximal.z))
else:
parent = int(segment.parent.segments)
proximalpoint = distpoints[parent]
cell_id_vs_seg_id_vs_proximal[cell.id][id] = cell_id_vs_seg_id_vs_distal[cell.id][parent]
proxpoints[id] = proximalpoint
shape = "cone"
if proximalpoint == distalpoint:
shape = "sphere"
proximalpoint = ""
if ( shape == "cone" and (proximalpoint.split('>')[0] == distalpoint.split('>')[0])):
comment = "Ignoring zero length segment (id = %i): %s -> %s\n"%(id, proximalpoint, distalpoint)
print_comment_v(comment)
cells_file.write(" // "+comment)
else:
cells_file.write(" %s {\n"%shape)
cells_file.write(" %s\n"%distalpoint)
if len(proximalpoint): cells_file.write(" %s\n"%proximalpoint)
cells_file.write(" //%s_%s.%s\n"%('CELL_GROUP_NAME','0', id))
cells_file.write(" }\n")
cells_file.write(" pigment { color rgb <%f,%f,%f> }\n"%(random.random(),random.random(),random.random()))
cells_file.write("}\n\n")
if splitOut:
pov_file.write("#include \""+cf+"\"\n\n")
pov_file.write("#include \""+nf+"\"\n\n")
pov_file.write('''\n/*\n Defining a dummy cell to use when cell in population is not found in NeuroML file...\n*/\n#declare %s =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 5.000000
}
pigment { color rgb <1,0,0> }
}\n'''%_DUMMY_CELL)
pov_file.write('''\n/*\n Defining the spheres to use for end points of connections...\n*/\n#declare conn_start_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <0,1,0> }
}\n\n#declare conn_end_point =
union {
sphere {
<0.000000, 0.000000, 0.000000>, 3.000000
}
pigment { color rgb <1,0,0> }
}\n''')
positions = {}
popElements = nml_doc.networks[0].populations
pop_id_vs_cell = {}
print_comment_v("There are %i populations in the file"%len(popElements))
for pop in popElements:
name = pop.id
celltype = pop.component
instances = pop.instances
if pop.component in cell_id_vs_cell.keys():
#if cell_id_vs_cell.has_key(pop.component):
pop_id_vs_cell[pop.id] = cell_id_vs_cell[pop.component]
info = "Population: %s has %i positioned cells of type: %s"%(name,len(instances),celltype)
print_comment_v(info)
colour = "1"
for prop in pop.properties:
if prop.tag == 'color':
colour = prop.value
colour = colour.replace(" ", ",")
#print "Colour determined to be: "+colour
net_file.write("\n\n/* "+info+" */\n\n")
pop_positions = {}
if not celltype in declaredcells:
cell_definition = _DUMMY_CELL
minXc = 0
minYc = 0
minZc = 0
maxXc = 0
maxYc = 0
maxZc = 0
else:
cell_definition = declaredcells[celltype]
for instance in instances:
location = instance.location
id = int(instance.id)
net_file.write("object {\n")
net_file.write(" %s\n"%cell_definition)
x = float(location.x)
y = float(location.y)
z = float(location.z)
pop_positions[id] = (x,y,z)
if x+minXc<minX: minX=x+minXc
if y+minYc<minY: minY=y+minYc
if z+minZc<minZ: minZ=z+minZc
if x+maxXc>maxX: maxX=x+maxXc
if y+maxYc>maxY: maxY=y+maxYc
if z+maxZc>maxZ: maxZ=z+maxZc
net_file.write(" translate <%s, %s, %s>\n"%(x,y,z))
if colour == '1':
colour = "%f,%f,%f"%(random.random(),random.random(),random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }"%(colour))
net_file.write("\n //%s_%s\n"%(name, id))
net_file.write("}\n")
positions[name] = pop_positions
if len(instances) == 0 and int(pop.size>0):
info = "Population: %s has %i unpositioned cells of type: %s"%(name,pop.size,celltype)
print_comment_v(info)
colour = "1"
'''
if pop.annotation:
print dir(pop.annotation)
print pop.annotation.anytypeobjs_
print pop.annotation.member_data_items_[0].name
print dir(pop.annotation.member_data_items_[0])
for prop in pop.annotation.anytypeobjs_:
print prop
if len(prop.getElementsByTagName('meta:tag'))>0 and prop.getElementsByTagName('meta:tag')[0].childNodes[0].data == 'color':
#print prop.getElementsByTagName('meta:tag')[0].childNodes
colour = prop.getElementsByTagName('meta:value')[0].childNodes[0].data
colour = colour.replace(" ", ",")
elif prop.hasAttribute('tag') and prop.getAttribute('tag') == 'color':
colour = prop.getAttribute('value')
colour = colour.replace(" ", ",")
print "Colour determined to be: "+colour
'''
net_file.write("\n\n/* "+info+" */\n\n")
net_file.write("object {\n")
net_file.write(" %s\n"%cell_definition)
x = 0
y = 0
z = 0
if x+minXc<minX: minX=x+minXc
if y+minYc<minY: minY=y+minYc
if z+minZc<minZ: minZ=z+minZc
if x+maxXc>maxX: maxX=x+maxXc
if y+maxYc>maxY: maxY=y+maxYc
if z+maxZc>maxZ: maxZ=z+maxZc
net_file.write(" translate <%s, %s, %s>\n"%(x,y,z))
if colour == '1':
colour = "%f,%f,%f"%(random.random(),random.random(),random.random())
if colour is not None:
net_file.write(" pigment { color rgb <%s> }"%(colour))
net_file.write("\n //%s_%s\n"%(name, id))
net_file.write("}\n")
#print positions
if args.conns or args.conn_points: # Note: segment specific connections not implemented yet... i.e. connections from dends to axons...
#print_comment_v("************************\n*\n* Note: connection lines in 3D do not yet target dendritic locations!\n*\n************************")
for projection in nml_doc.networks[0].projections:
pre = projection.presynaptic_population
post = projection.postsynaptic_population
connections = projection.connections + projection.connection_wds
print_comment_v("Adding %i connections %s -> %s "%(len(connections),pre,post))
#print cell_id_vs_seg_id_vs_distal
#print cell_id_vs_seg_id_vs_proximal
for connection in connections:
pre_cell_id = connection.get_pre_cell_id()
post_cell_id = connection.get_post_cell_id()
pre_loc = (0,0,0)
if pre in positions.keys():# positions.has_key(pre):
if len(positions[pre])>0:
pre_loc = positions[pre][pre_cell_id]
post_loc = (0,0,0)
if post in positions.keys(): #positions.has_key(post):
post_loc = positions[post][post_cell_id]
#if pop_id_vs_cell.has_key(projection.presynaptic_population):
if projection.presynaptic_population in pop_id_vs_cell.keys():
pre_cell = pop_id_vs_cell[projection.presynaptic_population]
d = cell_id_vs_seg_id_vs_distal[pre_cell.id][int(connection.pre_segment_id)]
p = cell_id_vs_seg_id_vs_proximal[pre_cell.id][int(connection.pre_segment_id)]
m = [ p[i]+float(connection.pre_fraction_along)*(d[i]-p[i]) for i in [0,1,2] ]
print_comment("Pre point is %s, %s between %s and %s"%(m,connection.pre_fraction_along,p,d))
pre_loc = [ pre_loc[i]+m[i] for i in [0,1,2] ]
if projection.postsynaptic_population in pop_id_vs_cell.keys(): #has_key(projection.postsynaptic_population):
#if pop_id_vs_cell.has_key(projection.postsynaptic_population):
post_cell = pop_id_vs_cell[projection.postsynaptic_population]
d = cell_id_vs_seg_id_vs_distal[post_cell.id][int(connection.post_segment_id)]
p = cell_id_vs_seg_id_vs_proximal[post_cell.id][int(connection.post_segment_id)]
m = [ p[i]+float(connection.post_fraction_along)*(d[i]-p[i]) for i in [0,1,2] ]
print_comment("Post point is %s, %s between %s and %s"%(m,connection.post_fraction_along,p,d))
post_loc = [ post_loc[i]+m[i] for i in [0,1,2] ]
if post_loc != pre_loc:
info = "// Connection from %s:%s %s -> %s:%s %s\n"%(pre, pre_cell_id, pre_loc, post, post_cell_id, post_loc)
print_comment(info)
net_file.write("// %s"%info)
if args.conns:
net_file.write("cylinder { <%s,%s,%s>, <%s,%s,%s>, .5 pigment{color Grey}}\n"%(pre_loc[0],pre_loc[1],pre_loc[2], post_loc[0],post_loc[1],post_loc[2]))
if args.conn_points:
net_file.write("object { conn_start_point translate <%s,%s,%s> }\n"%(pre_loc[0],pre_loc[1],pre_loc[2]))
net_file.write("object { conn_end_point translate <%s,%s,%s> }\n"%(post_loc[0],post_loc[1],post_loc[2]))
plane = '''
plane {
y, vv(-1)
pigment {checker color rgb 1.0, color rgb 0.8 scale 20}
}
'''
footer='''
#declare minX = %f;
#declare minY = %f;
#declare minZ = %f;
#declare maxX = %f;
#declare maxY = %f;
#declare maxZ = %f;
#macro uu(xx)
0.5 * (maxX *(1+xx) + minX*(1-xx))
#end
#macro vv(xx)
0.5 * (maxY *(1+xx) + minY*(1-xx))
#end
#macro ww(xx)
0.5 * (maxZ *(1+xx) + minZ*(1-xx))
#end
light_source {
<uu(5),uu(2),uu(5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(5),uu(-2),uu(-5)>
color rgb <1,1,1>
}
light_source {
<uu(-5),uu(-2),uu(5)>
color rgb <1,1,1>
}
// Trying to view box
camera {
location < uu(%s + %s * sin (clock * 2 * 3.141)) , vv(%s + %s * sin (clock * 2 * 3.141)) , ww(%s + %s * cos (clock * 2 * 3.141)) >
look_at < uu(%s + 0) , vv(%s + 0.05+0.3*sin (clock * 2 * 3.141)) , ww(%s + 0)>
}
%s
\n'''%(minX,minY,minZ,maxX,maxY,maxZ, args.posx, args.scalex, args.posy, args.scaley, args.posz, args.scalez, args.viewx, args.viewy, args.viewz, (plane if args.plane else "")) ### end of footer
pov_file.write(footer)
pov_file.close()
if args.movie:
ini_file_name = pov_file_name.replace(".pov", "_movie.ini")
ini_movie = '''
Antialias=On
+W800 +H600
Antialias_Threshold=0.3
Antialias_Depth=4
Input_File_Name=%s
Initial_Frame=1
Final_Frame=%i
Initial_Clock=0
Final_Clock=1
Cyclic_Animation=on
Pause_when_Done=off
'''
ini_file = open(ini_file_name, 'w')
ini_file.write(ini_movie%(pov_file_name, args.frames))
ini_file.close()
print_comment_v("Created file for generating %i movie frames at: %s. To run this type:\n\n povray %s\n"%(args.frames,ini_file_name,ini_file_name))
else:
print_comment_v("Created file for generating image of network. To run this type:\n\n povray %s\n"%(pov_file_name))
print_comment_v("Or for higher resolution:\n\n povray Antialias=On Antialias_Depth=10 Antialias_Threshold=0.1 +W1200 +H900 %s\n"%(pov_file_name))
def generate_lems_file_for_neuroml(
sim_id,
neuroml_file,
target,
duration,
dt,
lems_file_name,
target_dir,
gen_plots_for_all_v=True,
plot_all_segments=False,
gen_plots_for_only=[], # List of populations
gen_plots_for_quantities={}, # Dict with displays vs lists of quantity paths
gen_saves_for_all_v=True,
save_all_segments=False,
gen_saves_for_only=[], # List of populations
gen_saves_for_quantities={}, # Dict with file names vs lists of quantity paths
copy_neuroml=True,
seed=None):
if seed:
random.seed(
seed
) # 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' %
(file_name_full, neuroml_file))
ls = LEMSSimulation(sim_id, duration, dt, target)
nml_doc = read_neuroml2_file(neuroml_file,
include_includes=True,
verbose=True)
quantities_saved = []
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 NeuroML file (%s) to: %s (%s)" %
(neuroml_file, target_dir, os.path.abspath(target_dir)))
if os.path.abspath(
os.path.dirname(neuroml_file)) != os.path.abspath(target_dir):
shutil.copy(neuroml_file, target_dir)
neuroml_file_name = os.path.basename(neuroml_file)
ls.include_neuroml2_file(neuroml_file_name, include_included=False)
for include in nml_doc.includes:
incl_curr = '%s/%s' % (os.path.dirname(neuroml_file), include.href)
print_comment_v(' - Including %s located at %s' %
(include.href, incl_curr))
shutil.copy(incl_curr, target_dir)
ls.include_neuroml2_file(include.href, include_included=False)
sub_doc = read_neuroml2_file(incl_curr)
for include in sub_doc.includes:
incl_curr = '%s/%s' % (os.path.dirname(neuroml_file),
include.href)
print_comment_v(' -- Including %s located at %s' %
(include.href, 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) > 0 or len(gen_saves_for_only) > 0:
for network in nml_doc.networks:
for population in network.populations:
quantity_template = "%s[%i]/v"
component = population.component
size = population.size
cell = None
segment_ids = []
if plot_all_segments:
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 + "/v"
size = len(population.instances)
if gen_plots_for_all_v or population.id in gen_plots_for_only:
print_comment(
'Generating %i plots for %s in population %s' %
(size, component, population.id))
disp0 = 'DispPop__%s' % population.id
ls.create_display(disp0, "Voltages of %s" % disp0, "-90",
"50")
for i in range(size):
if 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, "v in seg %i %s" %
(segment_id, safe_variable(quantity)),
quantity, "1mV", get_next_hex_color())
else:
quantity = quantity_template % (population.id, i)
ls.add_line_to_display(
disp0, "v %s" % safe_variable(quantity),
quantity, "1mV", get_next_hex_color())
if gen_saves_for_all_v or population.id in gen_saves_for_only:
print_comment(
'Saving %i values of v for %s in population %s' %
(size, component, population.id))
of0 = 'Volts_file__%s' % population.id
ls.create_output_file(
of0, "%s.%s.v.dat" % (sim_id, population.id))
for i in range(size):
if 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)
for display in gen_plots_for_quantities.keys():
quantities = gen_plots_for_quantities[display]
ls.create_display(display, "Plots of %s" % display, "-90", "50")
for q in quantities:
ls.add_line_to_display(display, safe_variable(q), q, "1",
get_next_hex_color())
for file_name in gen_saves_for_quantities.keys():
quantities = gen_saves_for_quantities[file_name]
ls.create_output_file(file_name, file_name)
for q in quantities:
ls.add_column_to_output_file(file_name, safe_variable(q), q)
ls.save_to_file(file_name=file_name_full)
return quantities_saved
