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 post_wta_jobs(nodes,
p_b_e_range,
p_x_e_range,
p_e_e_range,
p_e_i_range,
p_i_i_range,
p_i_e_range,
num_trials,
muscimol_amount=0 * nS,
injection_site=0,
start_nodes=True):
sim_params = simulation_params()
sim_params.muscimol_amount = muscimol_amount
sim_params.injection_site = injection_site
input_sum = 40.0
launcher = Launcher(nodes)
if start_nodes:
launcher.set_application_script(
os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
contrast_range = [0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
for p_b_e in p_b_e_range:
for p_x_e in p_x_e_range:
for p_e_e in p_e_e_range:
for p_e_i in p_e_i_range:
for p_i_i in p_i_i_range:
for p_i_e in p_i_e_range:
wta_params = default_params()
wta_params.p_b_e = p_b_e
wta_params.p_x_e = p_x_e
wta_params.p_e_e = p_e_e
wta_params.p_e_i = p_e_i
wta_params.p_i_i = p_i_i
wta_params.p_i_e = p_i_e
for i, contrast in enumerate(contrast_range):
inputs = np.zeros(2)
inputs[0] = (input_sum * (contrast + 1.0) /
2.0)
inputs[1] = input_sum - inputs[0]
for t in range(num_trials):
np.random.shuffle(inputs)
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)
launcher.add_job(
cmds,
log_file_template=log_file_template,
output_file=out_file)
def launch_background_freq_processes(nodes, background_freq_range, trials, start_nodes=True):
mat_file='/tmp/pySBI/data/rerw/subjects/value1_s1_t2.mat'
launcher=Launcher(nodes)
for background_freq in background_freq_range:
for trial in range(trials):
cmds, log_file_template, out_file=get_rerw_commands(mat_file, 0*pA, 0*pA, 0*second, 0.4, 5.0,
background_freq, e_desc='background_freq.%.3f.trial.%d' % (background_freq,trial))
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
if start_nodes:
launcher.set_application_script(os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
def post_wta_jobs(nodes, p_b_e_range, p_x_e_range, p_e_e_range, p_e_i_range, p_i_i_range, p_i_e_range, num_trials,
muscimol_amount=0*nS, injection_site=0, start_nodes=True):
sim_params=simulation_params()
sim_params.muscimol_amount=muscimol_amount
sim_params.injection_site=injection_site
input_sum=40.0
launcher=Launcher(nodes)
if start_nodes:
launcher.set_application_script(os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
contrast_range=[0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
for p_b_e in p_b_e_range:
for p_x_e in p_x_e_range:
for p_e_e in p_e_e_range:
for p_e_i in p_e_i_range:
for p_i_i in p_i_i_range:
for p_i_e in p_i_e_range:
wta_params=default_params()
wta_params.p_b_e=p_b_e
wta_params.p_x_e=p_x_e
wta_params.p_e_e=p_e_e
wta_params.p_e_i=p_e_i
wta_params.p_i_i=p_i_i
wta_params.p_i_e=p_i_e
for i,contrast in enumerate(contrast_range):
inputs=np.zeros(2)
inputs[0]=(input_sum*(contrast+1.0)/2.0)
inputs[1]=input_sum-inputs[0]
for t in range(num_trials):
np.random.shuffle(inputs)
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)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
def launch_baseline_virtual_subject_processes(nodes, data_dir, num_real_subjects, virtual_subj_ids, start_nodes=True):
alpha_range=(0.0, 1.0)
beta_range=(1.69,6.63)
# Setup launcher
launcher=Launcher(nodes)
# For each virtual subject
for virtual_subj_id in virtual_subj_ids:
# Choose an actual subject
stim_file_name=None
control_file_name=None
while True:
i=np.random.choice(range(num_real_subjects))
subj_id=i+1
subj_stim_session_number=stim_order[i,LAT]
stim_file_name='value%d_s%d_t2.mat' % (subj_id,subj_stim_session_number)
subj_control_session_number=stim_order[i,NOSTIM1]
control_file_name='value%d_s%d_t2.mat' % (subj_id,subj_control_session_number)
if os.path.exists(os.path.join(data_dir,stim_file_name)) and\
os.path.exists(os.path.join(data_dir,control_file_name)):
break
# Sample alpha from subject distribution
alpha=alpha_range[0]+np.random.rand()*(alpha_range[1]-alpha_range[0])
# Sample beta from subject distribution - don't use subjects with high alpha
beta=beta_range[0]+np.random.rand()*(beta_range[1]-beta_range[0])
cmds, log_file_template, out_file=get_rerw_commands(control_file_name, 0*pA, 0*pA, 0*second, alpha, beta, None,
e_desc='baseline.virtual_subject.%d.control' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, 4*pA, -2*pA, 0*second, alpha, beta, None,
e_desc='baseline.virtual_subject.%d.anode' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, -4*pA, 2*pA, 0*second, alpha, beta, None,
e_desc='baseline.virtual_subject.%d.cathode' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, 2*pA, -4*pA, 0*second, alpha, beta, None,
e_desc='baseline.virtual_subject.%d.anode_control_1' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, -2*pA, 4*pA, 0*second, alpha, beta, None,
e_desc='baseline.virtual_subject.%d.cathode_control_1' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
if start_nodes:
launcher.set_application_script(os.path.join(SRC_DIR, 'sh/ezrcluster-application-script.sh'))
launcher.start_nodes()
def launch_virtual_subject_processes(nodes, data_dir, num_real_subjects, virtual_subj_ids, behavioral_param_file,
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)
# 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:
# Choose an actual subject
stim_file_name=None
control_file_name=None
while True:
i=np.random.choice(range(num_real_subjects))
subj_id=i+1
subj_stim_session_number=stim_order[i,LAT]
stim_file_name='value%d_s%d_t2.mat' % (subj_id,subj_stim_session_number)
subj_control_session_number=stim_order[i,NOSTIM1]
control_file_name='value%d_s%d_t2.mat' % (subj_id,subj_control_session_number)
if os.path.exists(os.path.join(data_dir,stim_file_name)) and \
os.path.exists(os.path.join(data_dir,control_file_name)):
break
# Sample alpha from subject distribution - don't use subjects with high alpha
alpha_hist,alpha_bins=np.histogram(alpha_vals[np.where(alpha_vals<.99)[0]], density=True)
bin_width=alpha_bins[1]-alpha_bins[0]
alpha_bin=np.random.choice(alpha_bins[:-1], p=alpha_hist*bin_width)
alpha=alpha_bin+np.random.rand()*bin_width
# 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
cmds, log_file_template, out_file=get_rerw_commands(control_file_name, 0*pA, 0*pA, 0*second, alpha, beta, None,
e_desc='virtual_subject.%d.control' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, 4*pA, -2*pA, 0*second, alpha, beta, None,
e_desc='virtual_subject.%d.anode' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, -4*pA, 2*pA, 0*second, alpha, beta, None,
e_desc='virtual_subject.%d.cathode' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, 2*pA, -4*pA, 0*second, alpha, beta, None,
e_desc='virtual_subject.%d.anode_control_1' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
cmds, log_file_template, out_file=get_rerw_commands(stim_file_name, -2*pA, 4*pA, 0*second, alpha, beta, None,
e_desc='virtual_subject.%d.cathode_control_1' % virtual_subj_id)
launcher.add_job(cmds, log_file_template=log_file_template, output_file=out_file)
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()