def launch_virtual_subject_processes(nodes, mu_0, virtual_subj_ids, behavioral_param_file, trials, stim_conditions,
start_nodes=True):
"""
nodes = nodes to run simulation on
data_dir = directory containing subject data
num_real_subjects = number of real subjects
num_virtual_subjects = number of virtual subjects to run
behavioral_param_file = file containing subject fitted behavioral parameters
start_nodes = whether or not to start nodes
"""
# Setup launcher
launcher=Launcher(nodes)
wta_params=default_params()
wta_params.mu_0=mu_0
wta_params.p_a=wta_params.mu_0/100.0
wta_params.p_b=wta_params.p_a
# Get subject alpha and beta values
f = h5py.File(behavioral_param_file)
control_group=f['control']
alpha_vals=np.array(control_group['alpha'])
beta_vals=np.array(control_group['beta'])
# For each virtual subject
for virtual_subj_id in virtual_subj_ids:
# Sample beta from subject distribution - don't use subjects with high alpha
beta_hist,beta_bins=np.histogram(beta_vals[np.where(alpha_vals<.99)[0]], density=True)
bin_width=beta_bins[1]-beta_bins[0]
beta_bin=np.random.choice(beta_bins[:-1], p=beta_hist*bin_width)
beta=beta_bin+np.random.rand()*bin_width
wta_params.background_freq=(beta-161.08)/-.17
contrast_range=[0.0, .032, .064, .096, .128, .256, .512]
for i,contrast in enumerate(contrast_range):
inputs=np.array([wta_params.mu_0+wta_params.p_a*contrast*100.0,
wta_params.mu_0-wta_params.p_b*contrast*100.0])
for t in range(trials):
np.random.shuffle(inputs)
for stim_condition,stim_values in stim_conditions.iteritems():
sim_params=simulation_params()
sim_params.p_dcs=stim_values[0]
sim_params.i_dcs=stim_values[1]
cmds,log_file_template,out_file=get_wta_cmds(wta_params, inputs, sim_params, contrast, t,
record_lfp=True, record_voxel=True, record_neuron_state=False, record_firing_rate=True,
record_spikes=True, save_summary_only=False,
e_desc='virtual_subject.%d.%s' % (virtual_subj_id,stim_condition))
launcher.add_batch_job(cmds, log_file_template=log_file_template, output_file=out_file)
launcher.post_jobs()
if start_nodes:
launcher.set_application_script(os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
def launch_control_virtual_subject_processes(nodes, mu_0, virtual_subj_ids, behavioral_param_file, trials,
stim_gains=[8,6,4,2,1,0.5,0.25], start_nodes=True):
"""
Launch stimulation intensity simulations with DCS applied only to pyramidal population
nodes = nodes to run simulation on
data_dir = directory containing subject data
num_real_subjects = number of real subjects
num_virtual_subjects = number of virtual subjects to run
behavioral_param_file = file containing subject fitted behavioral parameters
start_nodes = whether or not to start nodes
"""
# Setup launcher
launcher=Launcher(nodes)
# Get subject alpha and beta values
f = h5py.File(behavioral_param_file)
control_group=f['control']
alpha_vals=np.array(control_group['alpha'])
beta_vals=np.array(control_group['beta'])
# For each virtual subject
for virtual_subj_id in virtual_subj_ids:
wta_params=default_params()
wta_params.mu_0=mu_0
wta_params.p_a=mu_0/100.0
wta_params.p_b=wta_params.p_a
# Sample beta from subject distribution - don't use subjects with high alpha
beta_hist,beta_bins=np.histogram(beta_vals[np.where(alpha_vals<.99)[0]], density=True)
bin_width=beta_bins[1]-beta_bins[0]
beta_bin=np.random.choice(beta_bins[:-1], p=beta_hist*bin_width)
beta=beta_bin+np.random.rand()*bin_width
wta_params.background_freq=(beta-161.08)/-.17
contrast_range=[0.0, .032, .064, .128, .256, .512]
for i,contrast in enumerate(contrast_range):
inputs=np.zeros(2)
inputs[0]=wta_params.mu_0+wta_params.p_a*contrast*100.0
inputs[1]=wta_params.mu_0-wta_params.p_b*contrast*100.0
for t in range(trials):
np.random.shuffle(inputs)
for idx, stim_gain in enumerate(stim_gains):
sim_params=simulation_params()
sim_params.p_dcs=stim_gain*pA
cmds,log_file_template,out_file=get_wta_cmds(wta_params, inputs, sim_params, contrast, t,
record_lfp=True, record_voxel=True, record_neuron_state=False, record_firing_rate=True,
record_spikes=True, save_summary_only=False,
e_desc='virtual_subject.%d.anode' % virtual_subj_id)
launcher.add_batch_job(cmds, log_file_template=log_file_template, output_file=out_file)
sim_params=simulation_params()
sim_params.p_dcs=-stim_gain*pA
cmds,log_file_template,out_file=get_wta_cmds(wta_params, inputs, sim_params, contrast, t,
record_lfp=True, record_voxel=True, record_neuron_state=False, record_firing_rate=True,
record_spikes=True, save_summary_only=False,
e_desc='virtual_subject.%d.cathode' % virtual_subj_id)
launcher.add_batch_job(cmds, log_file_template=log_file_template, output_file=out_file)
if idx==0:
sim_params=simulation_params()
cmds,log_file_template,out_file=get_wta_cmds(wta_params, inputs, sim_params, contrast, t,
record_lfp=True, record_voxel=True, record_neuron_state=False, record_firing_rate=True,
record_spikes=True, save_summary_only=False,
e_desc='virtual_subject.%d.control' % virtual_subj_id)
launcher.add_batch_job(cmds, log_file_template=log_file_template, output_file=out_file)
launcher.post_jobs()
if start_nodes:
launcher.set_application_script(os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
