def compare(sim_data_file, show_plot_already=True, dataset=471141261):
dat_file_name = '../data/%s.dat' % dataset
x = []
y = []
colors = []
linestyles = []
data, indeces = pynml.reload_standard_dat_file(dat_file_name)
for ii in indeces:
x.append(data['t'])
y.append(data[ii])
colors.append('lightgrey')
linestyles.append('-')
data, indeces = pynml.reload_standard_dat_file(sim_data_file)
r = lambda: random.randint(0, 255)
for ii in indeces:
x.append(data['t'])
y.append(data[ii])
c = '#%02X%02X%02X' % (r(), r(), r())
colors.append(c)
linestyles.append('-')
pynml.generate_plot(x,
y,
"Comparing tuned cell to: %s" % dat_file_name,
xaxis='Input current (nA)',
yaxis='Membrane potential (mV)',
colors=colors,
linestyles=linestyles,
show_plot_already=show_plot_already)
def compare(sim_data_file, show_plot_already=True, dataset=471141261):
dat_file_name = '../data/%s.dat'%dataset
x = []
y = []
colors = []
linestyles = []
data, indeces = pynml.reload_standard_dat_file(dat_file_name)
for ii in indeces:
x.append(data['t'])
y.append(data[ii])
colors.append('lightgrey')
linestyles.append('-')
data, indeces = pynml.reload_standard_dat_file(sim_data_file)
r = lambda: random.randint(0,255)
for ii in indeces:
x.append(data['t'])
y.append(data[ii])
c = '#%02X%02X%02X' % (r(),r(),r())
colors.append(c)
linestyles.append('-')
pynml.generate_plot(x,
y,
"Comparing tuned cell to: %s"%dat_file_name,
xaxis = 'Input current (nA)',
yaxis = 'Membrane potential (mV)',
colors = colors,
linestyles = linestyles,
show_plot_already=show_plot_already)
def generate_lems(glif_dir, curr_pA, show_plot=True):
os.chdir(glif_dir)
with open('model_metadata.json', "r") as json_file:
model_metadata = json.load(json_file)
with open('neuron_config.json', "r") as json_file:
neuron_config = json.load(json_file)
with open('ephys_sweeps.json', "r") as json_file:
ephys_sweeps = json.load(json_file)
template_cell = '''<Lems>
<%s %s/>
</Lems>
'''
type = '???'
print(model_metadata['name'])
if '(LIF)' in model_metadata['name']:
type = 'glifCell'
if '(LIF-ASC)' in model_metadata['name']:
type = 'glifAscCell'
if '(LIF-R)' in model_metadata['name']:
type = 'glifRCell'
if '(LIF-R-ASC)' in model_metadata['name']:
type = 'glifRAscCell'
if '(LIF-R-ASC-A)' in model_metadata['name']:
type = 'glifRAscATCell'
cell_id = 'GLIF_%s'%glif_dir
attributes = ""
attributes +=' id="%s"'%cell_id
attributes +='\n C="%s F"'%neuron_config["C"]
attributes +='\n leakReversal="%s V"'%neuron_config["El"]
attributes +='\n reset="%s V"'%neuron_config["El"]
attributes +='\n thresh="%s V"'%( float(neuron_config["th_inf"]) * float(neuron_config["coeffs"]["th_inf"]))
attributes +='\n leakConductance="%s S"'%(1/float(neuron_config["R_input"]))
if 'Asc' in type:
attributes +='\n tau1="%s s"'%neuron_config["asc_tau_array"][0]
attributes +='\n tau2="%s s"'%neuron_config["asc_tau_array"][1]
attributes +='\n amp1="%s A"'% ( float(neuron_config["asc_amp_array"][0]) * float(neuron_config["coeffs"]["asc_amp_array"][0]) )
attributes +='\n amp2="%s A"'% ( float(neuron_config["asc_amp_array"][1]) * float(neuron_config["coeffs"]["asc_amp_array"][1]) )
if 'glifR' in type:
attributes +='\n bs="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_spike"]
attributes +='\n deltaThresh="%s V"'%neuron_config["threshold_dynamics_method"]["params"]["a_spike"]
attributes +='\n fv="%s"'%neuron_config["voltage_reset_method"]["params"]["a"]
attributes +='\n deltaV="%s V"'%neuron_config["voltage_reset_method"]["params"]["b"]
if 'glifRAscATCell' in type:
attributes +='\n bv="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_voltage"]
attributes +='\n a="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["a_voltage"]
file_contents = template_cell%(type, attributes)
print(file_contents)
cell_file_name = '%s.xml'%(cell_id)
cell_file = open(cell_file_name,'w')
cell_file.write(file_contents)
cell_file.close()
import opencortex.build as oc
nml_doc, network = oc.generate_network("Test_%s"%glif_dir)
pop = oc.add_single_cell_population(network,
'pop_%s'%glif_dir,
cell_id)
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="100ms",
duration="1000ms",
amplitude="%s pA"%curr_pA)
oc.add_inputs_to_population(network,
"Stim0",
pop,
pg.id,
all_cells=True)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
thresh = 'thresh'
if 'glifR' in type:
thresh = 'threshTotal'
lems_file_name = oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
include_extra_lems_files = [cell_file_name,'../GLIFs.xml'],
duration = 1200,
dt = 0.01,
gen_saves_for_quantities = {'thresh.dat':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]},
gen_plots_for_quantities = {'Threshold':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]})
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True)
print("Ran simulation; results reloaded for: %s"%results.keys())
info = "Model %s; %spA stimulation"%(glif_dir,curr_pA)
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/v'%(glif_dir,glif_dir)]]
labels = ['LEMS - jNeuroML']
original_model_v = 'original.v.dat'
if os.path.isfile(original_model_v):
data, indices = pynml.reload_standard_dat_file(original_model_v)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Membrane potential; %s"%info,
xaxis = "Time (s)",
yaxis = "Voltage (V)",
labels = labels,
grid = True,
show_plot_already=False,
save_figure_to='Comparison_%ipA.png'%(curr_pA))
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]]
labels = ['LEMS - jNeuroML']
original_model_th = 'original.thresh.dat'
if os.path.isfile(original_model_th):
data, indeces = pynml.reload_standard_dat_file(original_model_th)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Threshold; %s"%info,
xaxis = "Time (s)",
yaxis = "Voltage (V)",
labels = labels,
grid = True,
show_plot_already=show_plot,
save_figure_to='Comparison_Threshold_%ipA.png'%(curr_pA))
readme = '''
## Model: %(id)s
### Original model
%(name)s
[Allen Cell Types DB electrophysiology page for specimen](http://celltypes.brain-map.org/mouse/experiment/electrophysiology/%(spec)s)
[Neuron configuration](neuron_config.json); [model metadata](model_metadata.json); [electrophysiology summary](ephys_sweeps.json)
#### Original traces:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)
### Conversion to NeuroML 2
LEMS version of this model: [GLIF_%(id)s.xml](GLIF_%(id)s.xml)
[Definitions of LEMS Component Types](../GLIFs.xml) for GLIFs.
This model can be run locally by installing [jNeuroML](https://github.com/NeuroML/jNeuroML) and running:
jnml LEMS_Test_%(id)s.xml
#### Comparison:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)'''
readme_file = open('README.md','w')
curr_str = str(curr_pA)
# @type curr_str str
if curr_str.endswith('.0'):
curr_str = curr_str[:-2]
readme_file.write(readme%{"id":glif_dir,"name":model_metadata['name'],"spec":model_metadata["specimen_id"],"curr":curr_str})
readme_file.close()
os.chdir('..')
return model_metadata, neuron_config, ephys_sweeps
def analyse_cell(dataset_id, type, info, nogui = False, densities=False, analysis_dir='../../data/'):
reference = '%s_%s'%(type,dataset_id)
cell_file = '%s/%s.cell.nml'%(type,reference)
print("====================================\n\n Analysing cell: %s, dataset %s\n"%(cell_file,dataset_id))
nml_doc = pynml.read_neuroml2_file(cell_file)
notes = nml_doc.cells[0].notes if len(nml_doc.cells)>0 else nml_doc.izhikevich2007_cells[0].notes
meta_nml = eval(notes[notes.index('{'):])
summary = "Fitness: %s (max evals: %s, pop: %s)"%(meta_nml['fitness'],meta_nml['max_evaluations'],meta_nml['population_size'])
print summary
images = 'summary/%s_%s.png'
if_iv_data_files = 'summary/%s_%s.dat'
data, v_sub, curents_sub, freqs, curents_spike = get_if_iv_for_dataset('%s%s_analysis.json'%(analysis_dir,dataset_id))
if densities:
dataset = {}
seed = meta_nml['seed']
if isinstance(seed, tuple):
seed = seed[0]
layer = str(data['location'].split(',')[-1].strip().replace(' ',''))
ref = '%s_%s_%s'%(dataset_id,layer,int(seed))
dataset['id'] = dataset_id
dataset['reference'] = ref
metas = ['aibs_cre_line','aibs_dendrite_type','location']
for m in metas:
dataset[m] = str(data[m])
metas2 = ['fitness','population_size','seed']
for m in metas2:
dataset[m] = meta_nml[m]
# Assume images below already generated...
if type=='HH':
cell = nml_doc.cells[0]
sgv_files, all_info = generate_channel_density_plots(cell_file, text_densities=True, passives_erevs=True)
sgv_file =sgv_files[0]
for c in all_info:
if c == cell.id:
cc = 'tuned_cell_info'
else:
cc = c
dataset[cc] = all_info[c]
info['datasets'][ref] = dataset
elif type=='Izh':
dataset['tuned_cell_info'] = {}
izh_cell = nml_doc.izhikevich2007_cells[0]
for p in ['C','a','b','c','d','k','vpeak','vr','vt']:
dataset['tuned_cell_info'][p] = get_value_in_si(getattr(izh_cell, p))
'''
sgv_files, all_info = generate_channel_density_plots(cell_file, text_densities=True, passives_erevs=True)
sgv_file =sgv_files[0]
for c in all_info:
if c == cell.id:
cc = 'tuned_cell_info'
else:
cc = c
dataset[cc] = all_info[c]'''
info['datasets'][ref] = dataset
else:
traces_ax, if_ax, iv_ax = generate_current_vs_frequency_curve(cell_file,
reference,
simulator = 'jNeuroML_NEURON',
start_amp_nA = -0.1,
end_amp_nA = 0.4,
step_nA = 0.01,
analysis_duration = 1000,
analysis_delay = 50,
plot_voltage_traces = False,
plot_if = not nogui,
plot_iv = not nogui,
xlim_if = [-200, 400],
ylim_if = [-10, 120],
xlim_iv = [-200, 400],
ylim_iv = [-120, -40],
save_if_figure_to = images%(reference, 'if'),
save_iv_figure_to = images%(reference, 'iv'),
save_if_data_to = if_iv_data_files%(reference, 'if'),
save_iv_data_to = if_iv_data_files%(reference, 'iv'),
show_plot_already = False,
return_axes = True)
iv_ax.plot(curents_sub, v_sub, color='#ff2222',marker='o', linestyle='',zorder=1)
if_ax.plot(curents_spike, freqs ,color='#ff2222',marker='o', linestyle='',zorder=1)
iv_ax.get_figure().savefig(images%(reference, 'iv'),bbox_inches='tight')
if_ax.get_figure().savefig(images%(reference, 'if'),bbox_inches='tight')
offset = 100 # mV
ifv_x = []
ifv_y = []
markers = []
lines = []
colors = []
cols = {'Izh':'r','HH':'g','AllenHH':'b'}
for ii in ['if','iv']:
for tt in ['Izh','HH','AllenHH']:
rr = '%s_%s'%(tt,dataset_id)
f = if_iv_data_files%(rr, ii)
if os.path.isfile(f):
print("--- Opening: %s"%f)
data, indeces = reload_standard_dat_file(f)
ifv_x.append(data['t'])
if ii=='if':
ifv_y.append([ff-offset for ff in data[0]])
else:
ifv_y.append([vv for vv in data[0]])
markers.append('')
colors.append(cols[tt])
lines.append('-')
ifv_x.append(curents_sub)
vvsub = [vv for vv in v_sub]
ifv_y.append(vvsub)
sub_color = '#888888'
markers.append('D')
colors.append('k')
lines.append('')
ifv_x.append(curents_spike)
ifv_y.append([ff-offset for ff in freqs])
markers.append('o')
colors.append(sub_color)
lines.append('')
import matplotlib
import matplotlib.pyplot as plt
print ifv_x
print ifv_y
ylim = [-105, -20]
font_size = 18
ax1 = pynml.generate_plot(ifv_x,
ifv_y,
summary,
markers=markers,
colors=colors,
linestyles=lines,
show_plot_already=False,
xlim = [-100, 400],
font_size = font_size,
ylim = ylim,
title_above_plot=False)
plt.xlabel('Input current (pA)', fontsize = font_size)
plt.ylabel("Steady membrane potential (mV)", fontsize = font_size)
ax2 = ax1.twinx()
plt.ylim([ylim[0]+offset,ylim[1]+offset])
plt.ylabel('Firing frequency (Hz)', color=sub_color, fontsize = font_size)
ax2.tick_params(axis='y', colors=sub_color)
#plt.axis('off')
plt.savefig(images%(reference, 'if_iv'+"_FIG"),bbox_inches='tight')
temp_dir = 'temp/'
print("Copying %s to %s"%(cell_file, temp_dir))
shutil.copy(cell_file, temp_dir)
net_file = generate_network_for_sweeps(type, dataset_id, '%s.cell.nml'%(reference), reference, temp_dir, data_dir=analysis_dir)
lems_file_name = 'LEMS_Test_%s_%s.xml'%(type,dataset_id)
generate_lems_file_for_neuroml('Test_%s_%s'%(dataset_id,type),
net_file,
'network_%s_%s'%(dataset_id,type),
1500,
0.01,
lems_file_name,
temp_dir,
gen_plots_for_all_v=False,
copy_neuroml = False)
simulator = "jNeuroML_NEURON"
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(temp_dir+lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(temp_dir+lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False)
x = []
y = []
print results.keys()
tt = [t*1000 for t in results['t']]
for i in range(len(results)-1):
x.append(tt)
y.append([v*1000 for v in results['Pop0/%i/%s_%s/v'%(i,type,dataset_id)]])
pynml.generate_plot(x,
y,
summary,
show_plot_already=False,
ylim = [-120, 60],
save_figure_to = images%(reference, 'traces'),
title_above_plot=True)
ax = pynml.generate_plot(x,
y,
summary,
show_plot_already=False,
ylim = [-120, 60],
title_above_plot=False)
ax.set_xlabel(None)
ax.set_ylabel(None)
plt.axis('off')
fig_file = images%(reference, 'traces'+"_FIG")
plt.savefig(fig_file, bbox_inches='tight', pad_inches=0)
from PIL import Image
img = Image.open(fig_file)
img2 = img.crop((60, 40, 660, 480))
img2.save(fig_file)
def analyse_extracted_data():
analysed = []
analysed = [f for f in os.listdir('.') if (f.endswith('_analysis.json'))]
#analysed = [ f for f in os.listdir('.') if (f.endswith('_analysis.json')) ]
analysed.sort()
info = {}
info[
'info'] = 'Cell models tuned to data extracted from Cell Types Database. Note: these are preliminary models designed to test the framework for generating NeuroML models from this data, not the final, optimised models.'
info['datasets'] = []
to_include = None
#to_include = ['468120757']
#to_include = ['477127614','476686112']
#to_include = ['477127614']
#to_include = ['464198958']
for f in analysed:
if to_include == None or f.split('_')[0] in to_include:
print("*************************\n Looking at: %s" % f)
dataset = {}
info['datasets'].append(dataset)
'''
with open(f, "r") as json_file:
data = json.load(json_file)
id = data['data_set_id']
sweeps = data['sweeps']
print("Looking at data analysis in %s (dataset: %s)"%(f,id))
dataset['id'] = id
currents_v_sub = {}
currents_rate_spike = {}
for s in sweeps.keys():
current = float(sweeps[s]["sweep_metadata"]["aibs_stimulus_amplitude_pa"])
print("Sweep %s (%s pA)"%(s, current))
freq_key = '%s:mean_spike_frequency'%(s)
steady_state_key = '%s:average_1000_1200'%(s)
if sweeps[s]["pyelectro_iclamp_analysis"].has_key(freq_key):
currents_rate_spike[current] = sweeps[s]["pyelectro_iclamp_analysis"][freq_key]
else:
currents_rate_spike[current] = 0
currents_v_sub[current] = sweeps[s]["pyelectro_iclamp_analysis"][steady_state_key]
curents_sub = currents_v_sub.keys()
curents_sub.sort()
v = [currents_v_sub[c] for c in curents_sub]
'''
data, v_sub, curents_sub, v, curents_spike = get_if_iv_for_dataset(
f)
id = data['data_set_id']
dataset['id'] = data['data_set_id']
metas = ['aibs_cre_line', 'aibs_dendrite_type', 'location']
for m in metas:
dataset[m] = data[m]
target_file = 'summary/%s_%s.png'
pynml.generate_plot([curents_sub], [v_sub],
"Subthreshold responses: %s" % id,
colors=['k'],
linestyles=['-'],
markers=['o'],
xaxis="Current (pA)",
yaxis="Steady state (mV)",
xlim=[-200, 400],
ylim=[-120, -40],
grid=True,
show_plot_already=False,
save_figure_to=target_file %
('subthreshold', id))
#plt.plot(curents_sub, , color='k', linestyle='-', marker='o')
pynml.generate_plot([curents_spike], [v],
"Spiking frequencies: %s" % id,
colors=['k'],
linestyles=['-'],
markers=['o'],
xaxis="Current (pA)",
yaxis="Firing frequency (Hz)",
xlim=[-200, 400],
ylim=[-10, 120],
grid=True,
show_plot_already=False,
save_figure_to=target_file % ('spikes', id))
data0, indices = pynml.reload_standard_dat_file('%s.dat' % id)
x = []
y = []
tt = [t * 1000 for t in data0['t']]
for i in indices:
x.append(tt)
y.append([v * 1000 for v in data0[i]])
cols = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
ax = pynml.generate_plot(x,
y,
"Example traces from: %s" % id,
xaxis="Time (ms)",
yaxis="Membrane potential (mV)",
ylim=[-120, 60],
colors=cols,
show_plot_already=False,
save_figure_to=target_file %
('traces', id))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
sweeps = data['sweeps']
from data_helper import CURRENT_DATASETS, DATASET_TARGET_SWEEPS
i = 0
for s in DATASET_TARGET_SWEEPS[data['data_set_id']]:
current = float(sweeps[str(s)]["sweep_metadata"]
["aibs_stimulus_amplitude_pa"])
print("--- Sweep %s (%s pA)" % (s, current))
plt.text(1320,
+8 * i,
"%s pA" % (float('%.2f' % current)),
color=cols[i])
i += 1
plt.savefig(target_file % ('traces', id),
bbox_inches='tight',
pad_inches=0)
ax = pynml.generate_plot(x,
y,
"Example traces from: %s" % id,
ylim=[-120, 60],
colors=cols,
grid=False,
show_plot_already=False)
scale = 1 if not '477127614' in f else 1000
ax.plot([1300 * scale, 1300 * scale, 1500 * scale], [40, 20, 20],
color='k',
linewidth=5,
marker='',
solid_capstyle="butt",
solid_joinstyle='miter')
plt.axis('off')
fig_file = target_file % ('traces_FIG', id)
plt.savefig(fig_file, bbox_inches='tight', pad_inches=0)
from PIL import Image
img = Image.open(fig_file)
img2 = img.crop((60, 40, 660, 480))
img2.save(fig_file)
print(info)
make_html_file(info)
make_html_file(info, template=HTML_TEMPLATE_FILE_FIG, target='Figure.html')
make_md_file()
if not nogui:
plt.show()
def generate_lems(glif_dir, curr_pA, show_plot=True):
os.chdir(glif_dir)
with open('model_metadata.json', "r") as json_file:
model_metadata = json.load(json_file)
with open('neuron_config.json', "r") as json_file:
neuron_config = json.load(json_file)
with open('ephys_sweeps.json', "r") as json_file:
ephys_sweeps = json.load(json_file)
template_cell = '''<Lems>
<%s %s/>
</Lems>
'''
type = '???'
print(model_metadata['name'])
if '(LIF)' in model_metadata['name']:
type = 'glifCell'
if '(LIF-ASC)' in model_metadata['name']:
type = 'glifAscCell'
if '(LIF-R)' in model_metadata['name']:
type = 'glifRCell'
if '(LIF-R-ASC)' in model_metadata['name']:
type = 'glifRAscCell'
if '(LIF-R-ASC-A)' in model_metadata['name']:
type = 'glifRAscATCell'
cell_id = 'GLIF_%s' % glif_dir
attributes = ""
attributes += ' id="%s"' % cell_id
attributes += '\n C="%s F"' % neuron_config["C"]
attributes += '\n leakReversal="%s V"' % neuron_config["El"]
attributes += '\n reset="%s V"' % neuron_config["El"]
attributes += '\n thresh="%s V"' % (float(
neuron_config["th_inf"]) * float(neuron_config["coeffs"]["th_inf"]))
attributes += '\n leakConductance="%s S"' % (
1 / float(neuron_config["R_input"]))
if 'Asc' in type:
attributes += '\n tau1="%s s"' % neuron_config[
"asc_tau_array"][0]
attributes += '\n tau2="%s s"' % neuron_config[
"asc_tau_array"][1]
attributes += '\n amp1="%s A"' % (
float(neuron_config["asc_amp_array"][0]) *
float(neuron_config["coeffs"]["asc_amp_array"][0]))
attributes += '\n amp2="%s A"' % (
float(neuron_config["asc_amp_array"][1]) *
float(neuron_config["coeffs"]["asc_amp_array"][1]))
if 'glifR' in type:
attributes += '\n bs="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["b_spike"]
attributes += '\n deltaThresh="%s V"' % neuron_config[
"threshold_dynamics_method"]["params"]["a_spike"]
attributes += '\n fv="%s"' % neuron_config[
"voltage_reset_method"]["params"]["a"]
attributes += '\n deltaV="%s V"' % neuron_config[
"voltage_reset_method"]["params"]["b"]
if 'glifRAscATCell' in type:
attributes += '\n bv="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["b_voltage"]
attributes += '\n a="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["a_voltage"]
file_contents = template_cell % (type, attributes)
print(file_contents)
cell_file_name = '%s.xml' % (cell_id)
cell_file = open(cell_file_name, 'w')
cell_file.write(file_contents)
cell_file.close()
import opencortex.build as oc
nml_doc, network = oc.generate_network("Test_%s" % glif_dir)
pop = oc.add_single_cell_population(network, 'pop_%s' % glif_dir, cell_id)
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="100ms",
duration="1000ms",
amplitude="%s pA" % curr_pA)
oc.add_inputs_to_population(network, "Stim0", pop, pg.id, all_cells=True)
nml_file_name = '%s.net.nml' % network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
thresh = 'thresh'
if 'glifR' in type:
thresh = 'threshTotal'
lems_file_name = oc.generate_lems_simulation(
nml_doc,
network,
nml_file_name,
include_extra_lems_files=[cell_file_name, '../GLIFs.xml'],
duration=1200,
dt=0.01,
gen_saves_for_quantities={
'thresh.dat':
['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]
},
gen_plots_for_quantities={
'Threshold':
['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]
})
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True)
print("Ran simulation; results reloaded for: %s" % results.keys())
info = "Model %s; %spA stimulation" % (glif_dir, curr_pA)
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/v' % (glif_dir, glif_dir)]]
labels = ['LEMS - jNeuroML']
original_model_v = 'original.v.dat'
if os.path.isfile(original_model_v):
data, indices = pynml.reload_standard_dat_file(original_model_v)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Membrane potential; %s" % info,
xaxis="Time (s)",
yaxis="Voltage (V)",
labels=labels,
grid=True,
show_plot_already=False,
save_figure_to='Comparison_%ipA.png' % (curr_pA))
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]]
labels = ['LEMS - jNeuroML']
original_model_th = 'original.thresh.dat'
if os.path.isfile(original_model_th):
data, indeces = pynml.reload_standard_dat_file(original_model_th)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Threshold; %s" % info,
xaxis="Time (s)",
yaxis="Voltage (V)",
labels=labels,
grid=True,
show_plot_already=show_plot,
save_figure_to='Comparison_Threshold_%ipA.png' %
(curr_pA))
readme = '''
## Model: %(id)s
### Original model
%(name)s
[Allen Cell Types DB electrophysiology page for specimen](http://celltypes.brain-map.org/mouse/experiment/electrophysiology/%(spec)s)
[Neuron configuration](neuron_config.json); [model metadata](model_metadata.json); [electrophysiology summary](ephys_sweeps.json)
#### Original traces:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)
### Conversion to NeuroML 2
LEMS version of this model: [GLIF_%(id)s.xml](GLIF_%(id)s.xml)
[Definitions of LEMS Component Types](../GLIFs.xml) for GLIFs.
This model can be run locally by installing [jNeuroML](https://github.com/NeuroML/jNeuroML) and running:
jnml LEMS_Test_%(id)s.xml
#### Comparison:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)'''
readme_file = open('README.md', 'w')
curr_str = str(curr_pA)
# @type curr_str str
if curr_str.endswith('.0'):
curr_str = curr_str[:-2]
readme_file.write(
readme % {
"id": glif_dir,
"name": model_metadata['name'],
"spec": model_metadata["specimen_id"],
"curr": curr_str
})
readme_file.close()
os.chdir('..')
return model_metadata, neuron_config, ephys_sweeps
if '-summary' in sys.argv:
erefs = ['AllenHH_477127614', 'L23_Retuned_477127614',
'L23PyrRS'] #,'L23_Retuned_477127614']
irefs = ['AllenHH_476686112']
iv_xs = []
iv_ys = []
iv_labels = []
if_xs = []
if_ys = []
if_labels = []
for ref in erefs + irefs:
ivf = '%s_IV.dat' % ref
data, indices = pynml.reload_standard_dat_file(ivf)
print("Loaded %s from %s" % (data.keys(), ivf))
iv_xs.append(data['t'])
iv_ys.append(data[0])
iv_labels.append(ref)
iff = '%s_IF.dat' % ref
data, indices = pynml.reload_standard_dat_file(iff)
print("Loaded %s from %s" % (data.keys(), iff))
if_xs.append(data['t'])
if_ys.append(data[0])
if_labels.append(ref)
pynml.generate_plot(iv_xs,
iv_ys,
"IV curves",
#plot_cell_firing('../cells/AllenHH/AllenHH_477127614.cell.nml', num_processors=num_processors, show_plot=show)
#plot_cell_firing('../cells/AllenHH/AllenHH_476686112.cell.nml', num_processors=num_processors, show_plot=show)
#plot_cell_firing('../cells/BBP/cNAC187_L23_NBC_9d37c4b1f8_0_0.cell.nml', num_processors=num_processors, show_plot=show)
plot_cell_firing('../cells/SmithEtAl2013/L23_Retuned_477127614.cell.nml',
num_processors=num_processors,
show_plot=show)
refs = [('AllenHH_477127614', 'L23_Retuned_477127614'),
('AllenHH_476686112', 'cNAC187_L23_NBC_9d37c4b1f8_0_0')]
for ref in refs:
for ii in ['IF', 'IV']:
point_neuron, cols = pynml.reload_standard_dat_file('%s_%s.dat' %
(ref[0], ii))
detailed_neuron, cols = pynml.reload_standard_dat_file('%s_%s.dat' %
(ref[1], ii))
print detailed_neuron
pynml.generate_plot([point_neuron['t'], detailed_neuron['t']],
[point_neuron[0], detailed_neuron[0]],
"2 cells: %s, %s" % ref,
markers=['o', 'o'],
labels=[ref[0], ref[1]],
show_plot_already=False)
plt.show()
sim_dir = '../simulations/DefaultSimulationConfiguration__N/'
t_nrn_f = sim_dir + 'time.dat'
v_nrn_f = sim_dir + 'CG_CML_0.dat'
ca_nrn_f = sim_dir + 'CG_CML_0.cad_CONC_ca.dat'
x = []
y = []
xc = []
yc = []
if True or not l23:
data, indices = pynml.reload_standard_dat_file(v_nml2_f)
tt = [t * 1000 for t in data['t']]
x.append(tt)
y.append([v * 1000 for v in data[0]])
data, indices = pynml.reload_standard_dat_file(ca_nml2_f)
tt = [t * 1000 for t in data['t']]
xc.append(tt)
yc.append([c for c in data[0]])
def read_dat_file(filename):
f = open(filename)
sim_dir = "../simulations/DefaultSimulationConfiguration__N/"
t_nrn_f = sim_dir + "time.dat"
v_nrn_f = sim_dir + "CG_CML_0.dat"
ca_nrn_f = sim_dir + "CG_CML_0.cad_CONC_ca.dat"
x = []
y = []
xc = []
yc = []
if True or not l23:
data, indices = pynml.reload_standard_dat_file(v_nml2_f)
tt = [t * 1000 for t in data["t"]]
x.append(tt)
y.append([v * 1000 for v in data[0]])
data, indices = pynml.reload_standard_dat_file(ca_nml2_f)
tt = [t * 1000 for t in data["t"]]
xc.append(tt)
yc.append([c for c in data[0]])
def read_dat_file(filename):
f = open(filename)
def analyse_cell(dataset_id,
type,
info,
nogui=False,
densities=False,
analysis_dir='../../data/'):
reference = '%s_%s' % (type, dataset_id)
cell_file = '%s/%s.cell.nml' % (type, reference)
print(
"====================================\n\n Analysing cell: %s, dataset %s\n"
% (cell_file, dataset_id))
nml_doc = pynml.read_neuroml2_file(cell_file)
notes = nml_doc.cells[0].notes if len(
nml_doc.cells) > 0 else nml_doc.izhikevich2007_cells[0].notes
meta_nml = eval(notes[notes.index('{'):])
summary = "Fitness: %s (max evals: %s, pop: %s)" % (
meta_nml['fitness'], meta_nml['max_evaluations'],
meta_nml['population_size'])
print summary
images = 'summary/%s_%s.png'
if_iv_data_files = 'summary/%s_%s.dat'
data, v_sub, curents_sub, freqs, curents_spike = get_if_iv_for_dataset(
'%s%s_analysis.json' % (analysis_dir, dataset_id))
if densities:
dataset = {}
seed = meta_nml['seed']
if isinstance(seed, tuple):
seed = seed[0]
layer = str(data['location'].split(',')[-1].strip().replace(' ', ''))
ref = '%s_%s_%s' % (dataset_id, layer, int(seed))
dataset['id'] = dataset_id
dataset['reference'] = ref
metas = ['aibs_cre_line', 'aibs_dendrite_type', 'location']
for m in metas:
dataset[m] = str(data[m])
metas2 = ['fitness', 'population_size', 'seed']
for m in metas2:
dataset[m] = meta_nml[m]
# Assume images below already generated...
if type == 'HH':
cell = nml_doc.cells[0]
sgv_files, all_info = generate_channel_density_plots(
cell_file, text_densities=True, passives_erevs=True)
sgv_file = sgv_files[0]
for c in all_info:
if c == cell.id:
cc = 'tuned_cell_info'
else:
cc = c
dataset[cc] = all_info[c]
info['datasets'][ref] = dataset
elif type == 'Izh':
dataset['tuned_cell_info'] = {}
izh_cell = nml_doc.izhikevich2007_cells[0]
for p in ['C', 'a', 'b', 'c', 'd', 'k', 'vpeak', 'vr', 'vt']:
dataset['tuned_cell_info'][p] = get_value_in_si(
getattr(izh_cell, p))
'''
sgv_files, all_info = generate_channel_density_plots(cell_file, text_densities=True, passives_erevs=True)
sgv_file =sgv_files[0]
for c in all_info:
if c == cell.id:
cc = 'tuned_cell_info'
else:
cc = c
dataset[cc] = all_info[c]'''
info['datasets'][ref] = dataset
else:
traces_ax, if_ax, iv_ax = generate_current_vs_frequency_curve(
cell_file,
reference,
simulator='jNeuroML_NEURON',
start_amp_nA=-0.1,
end_amp_nA=0.4,
step_nA=0.01,
analysis_duration=1000,
analysis_delay=50,
plot_voltage_traces=False,
plot_if=not nogui,
plot_iv=not nogui,
xlim_if=[-200, 400],
ylim_if=[-10, 120],
xlim_iv=[-200, 400],
ylim_iv=[-120, -40],
save_if_figure_to=images % (reference, 'if'),
save_iv_figure_to=images % (reference, 'iv'),
save_if_data_to=if_iv_data_files % (reference, 'if'),
save_iv_data_to=if_iv_data_files % (reference, 'iv'),
show_plot_already=False,
return_axes=True)
iv_ax.plot(curents_sub,
v_sub,
color='#ff2222',
marker='o',
linestyle='',
zorder=1)
if_ax.plot(curents_spike,
freqs,
color='#ff2222',
marker='o',
linestyle='',
zorder=1)
iv_ax.get_figure().savefig(images % (reference, 'iv'),
bbox_inches='tight')
if_ax.get_figure().savefig(images % (reference, 'if'),
bbox_inches='tight')
offset = 100 # mV
ifv_x = []
ifv_y = []
markers = []
lines = []
colors = []
cols = {'Izh': 'r', 'HH': 'g', 'AllenHH': 'b'}
for ii in ['if', 'iv']:
for tt in ['Izh', 'HH', 'AllenHH']:
rr = '%s_%s' % (tt, dataset_id)
f = if_iv_data_files % (rr, ii)
if os.path.isfile(f):
print("--- Opening: %s" % f)
data, indeces = reload_standard_dat_file(f)
ifv_x.append(data['t'])
if ii == 'if':
ifv_y.append([ff - offset for ff in data[0]])
else:
ifv_y.append([vv for vv in data[0]])
markers.append('')
colors.append(cols[tt])
lines.append('-')
ifv_x.append(curents_sub)
vvsub = [vv for vv in v_sub]
ifv_y.append(vvsub)
sub_color = '#888888'
markers.append('D')
colors.append('k')
lines.append('')
ifv_x.append(curents_spike)
ifv_y.append([ff - offset for ff in freqs])
markers.append('o')
colors.append(sub_color)
lines.append('')
import matplotlib
import matplotlib.pyplot as plt
print ifv_x
print ifv_y
ylim = [-105, -20]
font_size = 18
ax1 = pynml.generate_plot(ifv_x,
ifv_y,
summary,
markers=markers,
colors=colors,
linestyles=lines,
show_plot_already=False,
xlim=[-100, 400],
font_size=font_size,
ylim=ylim,
title_above_plot=False)
plt.xlabel('Input current (pA)', fontsize=font_size)
plt.ylabel("Steady membrane potential (mV)", fontsize=font_size)
ax2 = ax1.twinx()
plt.ylim([ylim[0] + offset, ylim[1] + offset])
plt.ylabel('Firing frequency (Hz)',
color=sub_color,
fontsize=font_size)
ax2.tick_params(axis='y', colors=sub_color)
#plt.axis('off')
plt.savefig(images % (reference, 'if_iv' + "_FIG"),
bbox_inches='tight')
temp_dir = 'temp/'
print("Copying %s to %s" % (cell_file, temp_dir))
shutil.copy(cell_file, temp_dir)
net_file = generate_network_for_sweeps(type,
dataset_id,
'%s.cell.nml' % (reference),
reference,
temp_dir,
data_dir=analysis_dir)
lems_file_name = 'LEMS_Test_%s_%s.xml' % (type, dataset_id)
generate_lems_file_for_neuroml('Test_%s_%s' % (dataset_id, type),
net_file,
'network_%s_%s' % (dataset_id, type),
1500,
0.01,
lems_file_name,
temp_dir,
gen_plots_for_all_v=False,
copy_neuroml=False)
simulator = "jNeuroML_NEURON"
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(temp_dir + lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(
temp_dir + lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False)
x = []
y = []
print results.keys()
tt = [t * 1000 for t in results['t']]
for i in range(len(results) - 1):
x.append(tt)
y.append([
v * 1000
for v in results['Pop0/%i/%s_%s/v' % (i, type, dataset_id)]
])
pynml.generate_plot(x,
y,
summary,
show_plot_already=False,
ylim=[-120, 60],
save_figure_to=images % (reference, 'traces'),
title_above_plot=True)
ax = pynml.generate_plot(x,
y,
summary,
show_plot_already=False,
ylim=[-120, 60],
title_above_plot=False)
ax.set_xlabel(None)
ax.set_ylabel(None)
plt.axis('off')
fig_file = images % (reference, 'traces' + "_FIG")
plt.savefig(fig_file, bbox_inches='tight', pad_inches=0)
from PIL import Image
img = Image.open(fig_file)
img2 = img.crop((60, 40, 660, 480))
img2.save(fig_file)
import sys
from pyneuroml import pynml
cell = sys.argv[1]
print("Comparing behaviour of cell %s to original NEURON code" % cell)
orig_dat = '../%s.soma.dat' % cell
orig_results, indices = pynml.reload_standard_dat_file(orig_dat)
print("Reloaded NEURON data: %s" % orig_results.keys())
results = pynml.run_lems_with_jneuroml_neuron('LEMS_%s.xml' % cell,
nogui=True,
load_saved_data=True)
print("Reloaded data: %s" % results.keys())
xs = []
ys = []
labels = []
xs.append([t * 1000 for t in results['t']])
ys.append([v * 1000 for v in results['Pop_%scell/0/%scell/v' % (cell, cell)]])
labels.append('jNeuroML_NEURON')
xs.append(orig_results['t'])
ys.append(orig_results[0])
labels.append('NEURON')
pynml.generate_plot(xs, ys, 'Plot of %s' % cell, labels=labels)
from pyneuroml import pynml
import matplotlib.pyplot as plt
data, indices = pynml.reload_standard_dat_file("KAHP.states.dat")
x = []
y = []
for i in indices:
x.append(data["t"])
y.append(data[i])
totals = []
for j in range(len(data["t"])):
tot = 0
for i in indices:
tot += data[i][j]
totals.append(tot)
labels = indices
x.append(data["t"])
y.append(totals)
labels.append("total")
pynml.generate_plot(
x,
y,
"States",
xaxis="Time (ms)",
yaxis="State occupancy",
from pyneuroml import pynml
import matplotlib.pyplot as plt
files = ['NaV.states.dat', '../NEURON/Soma.states.dat']
for file in files:
data, indices = pynml.reload_standard_dat_file(file)
x = []
y = []
for i in indices:
x.append(data['t'])
y.append(data[i])
totals = []
for j in range(len(data['t'])):
tot = 0
for i in indices:
tot += data[i][j]
totals.append(tot)
labels = indices
x.append(data['t'])
y.append(totals)
labels.append('total')
pynml.generate_plot(x,
y,
"States from file: %s" % file,
xaxis="Time (ms)",
yaxis="State occupancy",
from pyneuroml import pynml
import matplotlib.pyplot as plt
data, indices = pynml.reload_standard_dat_file('KAHP.states.dat')
x = []
y = []
for i in indices:
x.append(data['t'])
y.append(data[i])
totals = []
for j in range(len(data['t'])):
tot = 0
for i in indices:
tot+=data[i][j]
totals.append(tot)
labels = indices
x.append(data['t'])
y.append(totals)
labels.append('total')
pynml.generate_plot(x,
y,
"States",
xaxis = "Time (ms)",
yaxis = "State occupancy",
from pyneuroml import pynml
import matplotlib.pyplot as plt
files = ['NaV.states.dat', '../NEURON/Soma.states.dat']
for file in files:
data, indices = pynml.reload_standard_dat_file(file)
x = []
y = []
for i in indices:
x.append(data['t'])
y.append(data[i])
totals = []
for j in range(len(data['t'])):
tot = 0
for i in indices:
tot+=data[i][j]
totals.append(tot)
labels = indices
x.append(data['t'])
y.append(totals)
labels.append('total')
pynml.generate_plot(x,
y,
"States from file: %s"%file,
lims2 = {'EPSC': (-7e-11, 0e-10), 'IPSC': (0e-8, 1.7e-10)}
for x in lims.keys():
print("=====\nPlot for %s" % x)
xs = []
ys = []
labels = []
all_seg_0 = []
all_seg_1406 = []
for f in os.listdir('./temp'):
if f.startswith('v_clamps') and x in f and f.endswith('dat'):
print('Adding %s' % f)
seed = f.split('.')[1]
seg = f.split('.')[0].split('_')[3]
res, i = pynml.reload_standard_dat_file('temp/%s' % f)
print res.keys()
time_points = res['t']
xs.append(res['t'])
ys.append(res[0])
labels.append('%s %s' % (seg, seed))
if seg == 'seg0':
all_seg_0.append(res[0])
if seg == 'seg1406':
all_seg_1406.append(res[0])
avg_seg0 = np.zeros(len(all_seg_0[0]))
for l in all_seg_0:
for i in range(len(l)):
avg_seg0[i] += l[i]
pynml.generate_plot([curents_spike],
[v],
"Spiking frequencies: %s"%id,
colors = ['k'],
linestyles=['-'],
markers=['o'],
xaxis = "Current (pA)",
yaxis = "Firing frequency (Hz)",
xlim = [-200, 400],
ylim = [-10, 120],
grid = True,
show_plot_already=False,
save_figure_to = target_file%('spikes', id))
data, indices = pynml.reload_standard_dat_file('%s.dat'%id)
x = []
y = []
tt = [t*1000 for t in data['t']]
for i in indices:
x.append(tt)
y.append([v*1000 for v in data[i]])
pynml.generate_plot(x,
y,
"Example traces from: %s"%id,
xaxis = "Time (ms)",
yaxis = "Membrane potential (mV)",
ylim = [-120, 60],
show_plot_already=False,
save_figure_to = target_file%('traces', id))