def create_wta_network_report(file_prefix,
contrast_range,
num_trials,
reports_dir,
edesc,
regenerate_network_plots=True,
regenerate_trial_plots=True):
make_report_dirs(reports_dir)
report_info = Struct()
report_info.edesc = edesc
report_info.trials = []
(data_dir, data_file_prefix) = os.path.split(file_prefix)
total_trials = num_trials * len(contrast_range)
trial_contrast = np.zeros([total_trials, 1])
trial_max_bold = np.zeros(total_trials)
trial_max_input = np.zeros([total_trials, 1])
trial_max_rate = np.zeros([total_trials, 1])
trial_rt = np.zeros([total_trials, 1])
report_info.contrast_accuracy = np.zeros([len(contrast_range), 1])
max_bold = []
for j, contrast in enumerate(contrast_range):
contrast_max_bold = []
report_info.contrast_accuracy[j] = 0.0
for i in range(num_trials):
file_name = '%s.contrast.%0.4f.trial.%d.h5' % (file_prefix,
contrast, i)
print('opening %s' % file_name)
data = FileInfo(file_name)
if not i:
report_info.wta_params = data.wta_params
report_info.voxel_params = data.voxel_params
report_info.num_groups = data.num_groups
report_info.trial_duration = data.trial_duration
report_info.background_rate = data.background_rate
report_info.stim_start_time = data.stim_start_time
report_info.stim_end_time = data.stim_end_time
report_info.network_group_size = data.network_group_size
report_info.background_input_size = data.background_input_size
report_info.task_input_size = data.task_input_size
trial_idx = j * num_trials + i
trial = create_trial_report(
data,
reports_dir,
contrast,
i,
regenerate_plots=regenerate_trial_plots)
trial_contrast[trial_idx] = trial.input_contrast
if not math.isnan(trial.max_bold):
trial_max_bold[trial_idx] = trial.max_bold
else:
if j > 1 and max_bold[j - 1] < 1.0 and max_bold[j - 2] < 1.0:
trial_max_bold[trial_idx] = max_bold[j - 1] + (
max_bold[j - 1] - max_bold[j - 2])
elif j > 0 and max_bold[j - 1] < 1.0:
trial_max_bold[trial_idx] = max_bold[j - 1] * 2.0
else:
trial_max_bold[trial_idx] = 1.0
report_info.contrast_accuracy[j] += trial.correct
trial_max_input[trial_idx] = trial.max_input
trial_max_rate[trial_idx] = trial.max_rate
trial_rt[trial_idx] = trial.rt
report_info.trials.append(trial)
contrast_max_bold.append(trial_max_bold[trial_idx])
report_info.contrast_accuracy[j] /= float(num_trials)
mean_contrast_bold = np.mean(np.array(contrast_max_bold))
max_bold.append(mean_contrast_bold)
clf = LinearRegression()
clf.fit(trial_max_input, trial_max_rate)
a = clf.coef_[0]
b = clf.intercept_
report_info.io_slope = a
report_info.io_intercept = b
report_info.io_r_sqr = clf.score(trial_max_input, trial_max_rate)
furl = 'img/input_output_rate.png'
fname = os.path.join(reports_dir, furl)
report_info.input_output_rate_url = furl
if regenerate_network_plots or not os.path.exists(fname):
fig = plt.figure()
plt.plot(trial_max_input, trial_max_rate, 'x')
x_min = np.min(trial_max_input)
x_max = np.max(trial_max_input)
plt.plot([x_min, x_max], [x_min, x_max], '--')
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Max Input Rate')
plt.ylabel('Max Population Rate')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir,
'img/input_output_rate.eps'))
plt.close()
report_info.bold = create_bold_report(
reports_dir,
trial_contrast,
trial_max_bold,
trial_max_rate,
trial_rt,
regenerate_plot=regenerate_network_plots)
report_info.roc = create_roc_report(
file_prefix,
report_info.num_groups,
contrast_range,
num_trials,
reports_dir,
regenerate_plot=regenerate_network_plots)
#create report
template_file = 'wta_network_instance.html'
env = Environment(loader=FileSystemLoader(TEMPLATE_DIR))
template = env.get_template(template_file)
output_file = 'wta_network.%s.html' % data_file_prefix
fname = os.path.join(reports_dir, output_file)
stream = template.stream(rinfo=report_info)
stream.dump(fname)
return report_info
def create_all_reports(data_dir,
num_groups,
trial_duration,
p_b_e_range,
p_x_e_range,
p_e_e_range,
p_e_i_range,
p_i_i_range,
p_i_e_range,
contrast_range,
num_trials,
e_desc,
base_report_dir,
regenerate_network_plots=True,
regenerate_trial_plots=True,
smooth_missing_params=False,
summary_filename='wta_network_summary.h5'):
make_report_dirs(base_report_dir)
summary_data = SummaryData(num_groups=num_groups,
num_trials=num_trials,
trial_duration=trial_duration,
p_b_e_range=p_b_e_range,
p_x_e_range=p_x_e_range,
p_e_e_range=p_e_e_range,
p_e_i_range=p_e_i_range,
p_i_i_range=p_i_i_range,
p_i_e_range=p_i_e_range)
bc_slope_dict = {}
bc_intercept_dict = {}
bc_r_sqr_dict = {}
auc_dict = {}
bfr_slope_dict = {}
bfr_intercept_dict = {}
bfr_r_sqr_dict = {}
param_combos = get_tested_param_combos(data_dir, num_groups,
trial_duration, contrast_range,
num_trials, e_desc)
report_info = Struct()
report_info.edesc = e_desc
report_info.roc_auc = {}
report_info.bc_slope = {}
report_info.bc_intercept = {}
report_info.bc_r_sqr = {}
report_info.bfr_slope = {}
report_info.bfr_intercept = {}
report_info.bfr_r_sqr = {}
for (p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e) in param_combos:
if p_b_e in p_b_e_range and p_x_e in p_x_e_range and p_e_e in p_e_e_range and p_e_i in p_e_i_range and p_i_i in p_i_i_range and p_i_e in p_i_e_range:
i = p_b_e_range.index(round(p_b_e, 3))
j = p_x_e_range.index(round(p_x_e, 3))
k = p_e_e_range.index(round(p_e_e, 3))
l = p_e_i_range.index(round(p_e_i, 3))
m = p_i_i_range.index(round(p_i_i, 3))
n = p_i_e_range.index(round(p_i_e, 3))
file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.%s' %\
(num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e, e_desc)
file_prefix = os.path.join(data_dir, file_desc)
reports_dir = os.path.join(base_report_dir, file_desc)
if all_trials_exist(file_prefix, contrast_range, num_trials):
print('Creating report for %s' % file_desc)
wta_report = create_wta_network_report(
file_prefix,
contrast_range,
num_trials,
reports_dir,
e_desc,
regenerate_network_plots=regenerate_network_plots,
regenerate_trial_plots=regenerate_trial_plots)
if not (i, j, k, l, m, n) in bc_slope_dict:
bc_slope_dict[(i, j, k, l, m, n)] = []
bc_slope_dict[(i, j, k, l, m,
n)].append(wta_report.bold.bold_contrast_slope)
if not (i, j, k, l, m, n) in bc_intercept_dict:
bc_intercept_dict[(i, j, k, l, m, n)] = []
bc_intercept_dict[(i, j, k, l, m, n)].append(
wta_report.bold.bold_contrast_intercept)
if not (i, j, k, l, m, n) in bc_r_sqr_dict:
bc_r_sqr_dict[(i, j, k, l, m, n)] = []
bc_r_sqr_dict[(i, j, k, l, m,
n)].append(wta_report.bold.bold_contrast_r_sqr)
if not (i, j, k, l, m, n) in auc_dict:
auc_dict[(i, j, k, l, m, n)] = []
auc_dict[(i, j, k, l, m, n)].append(wta_report.roc.auc)
if not (i, j, k, l, m, n) in bfr_slope_dict:
bfr_slope_dict[(i, j, k, l, m, n)] = []
bfr_slope_dict[(i, j, k, l, m, n)].append(
wta_report.bold.bold_firing_rate_slope)
if not (i, j, k, l, m, n) in bfr_intercept_dict:
bfr_intercept_dict[(i, j, k, l, m, n)] = []
bfr_intercept_dict[(i, j, k, l, m, n)].append(
wta_report.bold.bold_firing_rate_intercept)
if not (i, j, k, l, m, n) in bfr_r_sqr_dict:
bfr_r_sqr_dict[(i, j, k, l, m, n)] = []
bfr_r_sqr_dict[(i, j, k, l, m, n)].append(
wta_report.bold.bold_firing_rate_r_sqr)
report_info.roc_auc[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.roc.auc
report_info.bc_slope[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_contrast_slope
report_info.bc_intercept[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_contrast_intercept
report_info.bc_r_sqr[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_contrast_r_sqr
report_info.bfr_slope[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_firing_rate_slope
report_info.bfr_intercept[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_firing_rate_intercept
report_info.bfr_r_sqr[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = wta_report.bold.bold_firing_rate_r_sqr
else:
report_info.roc_auc[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.bc_slope[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.bc_intercept[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.bc_r_sqr[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.bfr_slope[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.bfr_intercept[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.brf_r_sqr[(p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = 0
report_info.num_groups = num_groups
report_info.trial_duration = trial_duration
report_info.num_trials = num_trials
report_info.p_b_e_range = p_b_e_range
report_info.p_x_e_range = p_x_e_range
report_info.p_e_e_range = p_e_e_range
report_info.p_e_i_range = p_e_i_range
report_info.p_i_i_range = p_i_i_range
report_info.p_i_e_range = p_i_e_range
summary_data.fill(auc_dict,
bc_slope_dict,
bc_intercept_dict,
bc_r_sqr_dict,
bfr_slope_dict,
bfr_intercept_dict,
bfr_r_sqr_dict,
smooth_missing_params=smooth_missing_params)
summary_data.write_to_file(os.path.join(base_report_dir, summary_filename))
bc_bayes_analysis = run_bayesian_analysis(
summary_data.auc, summary_data.bc_slope, summary_data.bc_intercept,
summary_data.bc_r_sqr, num_trials, p_b_e_range, p_e_e_range,
p_e_i_range, p_i_e_range, p_i_i_range, p_x_e_range)
bfr_bayes_analysis = run_bayesian_analysis(
summary_data.auc, summary_data.bfr_slope, summary_data.bfr_intercept,
summary_data.bfr_r_sqr, num_trials, p_b_e_range, p_e_e_range,
p_e_i_range, p_i_e_range, p_i_i_range, p_x_e_range)
bc_base_dir = os.path.join(base_report_dir, 'bold-contrast')
make_report_dirs(bc_base_dir)
render_summary_report(bc_base_dir, bc_bayes_analysis, p_b_e_range,
p_e_e_range, p_e_i_range, p_i_e_range, p_i_i_range,
p_x_e_range, report_info)
bfr_base_dir = os.path.join(base_report_dir, 'bold-firing_rate')
make_report_dirs(bfr_base_dir)
render_summary_report(bfr_base_dir, bfr_bayes_analysis, p_b_e_range,
p_e_e_range, p_e_i_range, p_i_e_range, p_i_i_range,
p_x_e_range, report_info)
def create_summary_report(summary_file_name, base_report_dir, e_desc):
make_report_dirs(base_report_dir)
summary_data=SummaryData()
summary_data.read_from_file(summary_file_name)
report_info=Struct()
report_info.edesc=e_desc
report_info.roc_auc={}
report_info.io_slope={}
report_info.io_intercept={}
report_info.io_r_sqr={}
report_info.bc_slope={}
report_info.bc_intercept={}
report_info.bc_r_sqr={}
report_info.bfr_slope={}
report_info.bfr_intercept={}
report_info.bfr_r_sqr={}
for i,p_b_e in enumerate(summary_data.p_b_e_range):
for j,p_x_e in enumerate(summary_data.p_x_e_range):
for k,p_e_e in enumerate(summary_data.p_e_e_range):
for l,p_e_i in enumerate(summary_data.p_e_i_range):
for m,p_i_i in enumerate(summary_data.p_i_i_range):
for n,p_i_e in enumerate(summary_data.p_i_e_range):
if summary_data.auc[i,j,k,l,m,n]>0:
report_info.roc_auc[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.auc[i,j,k,l,m,n]
report_info.bc_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bc_slope[i,j,k,l,m,n]
report_info.bc_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bc_intercept[i,j,k,l,m,n]
report_info.bc_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bc_r_sqr[i,j,k,l,m,n]
report_info.bfr_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bfr_slope[i,j,k,l,m,n]
report_info.bfr_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bfr_intercept[i,j,k,l,m,n]
report_info.bfr_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=summary_data.bfr_r_sqr[i,j,k,l,m,n]
else:
report_info.roc_auc[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bfr_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bfr_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bfr_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.num_groups=summary_data.num_groups
report_info.trial_duration=summary_data.trial_duration
report_info.num_trials=summary_data.num_trials
report_info.p_b_e_range=summary_data.p_b_e_range
report_info.p_x_e_range=summary_data.p_x_e_range
report_info.p_e_e_range=summary_data.p_e_e_range[:-1]
report_info.p_e_i_range=summary_data.p_e_i_range
report_info.p_i_i_range=summary_data.p_i_i_range
report_info.p_i_e_range=summary_data.p_i_e_range
bc_bayes_analysis=run_bayesian_analysis(summary_data.auc, summary_data.bc_slope, summary_data.bc_intercept,
summary_data.bc_r_sqr, summary_data.num_trials, summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range, summary_data.p_i_i_range, summary_data.p_x_e_range)
bfr_bayes_analysis=run_bayesian_analysis(summary_data.auc, summary_data.bfr_slope, summary_data.bfr_intercept,
summary_data.bfr_r_sqr, summary_data.num_trials, summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range, summary_data.p_i_i_range, summary_data.p_x_e_range)
bc_base_dir=os.path.join(base_report_dir, 'bold-contrast')
make_report_dirs(bc_base_dir)
render_summary_report(bc_base_dir, bc_bayes_analysis, summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range, summary_data.p_i_i_range, summary_data.p_x_e_range,
report_info)
bfr_base_dir=os.path.join(base_report_dir, 'bold-firing_rate')
make_report_dirs(bfr_base_dir)
render_summary_report(bfr_base_dir, bfr_bayes_analysis, summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range, summary_data.p_i_i_range, summary_data.p_x_e_range,
report_info)
def create_all_reports(data_dir, num_groups, trial_duration, p_b_e_range, p_x_e_range, p_e_e_range, p_e_i_range,
p_i_i_range, p_i_e_range, contrast_range, num_trials, e_desc, base_report_dir, regenerate_network_plots=True,
regenerate_trial_plots=True, smooth_missing_params=False,
summary_filename='wta_network_summary.h5'):
make_report_dirs(base_report_dir)
summary_data=SummaryData(num_groups=num_groups, num_trials=num_trials, trial_duration=trial_duration,
p_b_e_range=p_b_e_range, p_x_e_range=p_x_e_range, p_e_e_range=p_e_e_range, p_e_i_range=p_e_i_range,
p_i_i_range=p_i_i_range, p_i_e_range=p_i_e_range)
bc_slope_dict={}
bc_intercept_dict={}
bc_r_sqr_dict={}
auc_dict={}
bfr_slope_dict={}
bfr_intercept_dict={}
bfr_r_sqr_dict={}
param_combos=get_tested_param_combos(data_dir, num_groups, trial_duration, contrast_range, num_trials, e_desc)
report_info=Struct()
report_info.edesc=e_desc
report_info.roc_auc={}
report_info.bc_slope={}
report_info.bc_intercept={}
report_info.bc_r_sqr={}
report_info.bfr_slope={}
report_info.bfr_intercept={}
report_info.bfr_r_sqr={}
for (p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e) in param_combos:
if p_b_e in p_b_e_range and p_x_e in p_x_e_range and p_e_e in p_e_e_range and p_e_i in p_e_i_range and p_i_i in p_i_i_range and p_i_e in p_i_e_range:
i=p_b_e_range.index(round(p_b_e,3))
j=p_x_e_range.index(round(p_x_e,3))
k=p_e_e_range.index(round(p_e_e,3))
l=p_e_i_range.index(round(p_e_i,3))
m=p_i_i_range.index(round(p_i_i,3))
n=p_i_e_range.index(round(p_i_e,3))
file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.%s' %\
(num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e, e_desc)
file_prefix=os.path.join(data_dir,file_desc)
reports_dir=os.path.join(base_report_dir,file_desc)
if all_trials_exist(file_prefix, contrast_range, num_trials):
print('Creating report for %s' % file_desc)
wta_report=create_wta_network_report(file_prefix, contrast_range, num_trials, reports_dir,
e_desc, regenerate_network_plots=regenerate_network_plots, regenerate_trial_plots=regenerate_trial_plots)
if not (i,j,k,l,m,n) in bc_slope_dict:
bc_slope_dict[(i,j,k,l,m,n)]=[]
bc_slope_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_contrast_slope)
if not (i,j,k,l,m,n) in bc_intercept_dict:
bc_intercept_dict[(i,j,k,l,m,n)]=[]
bc_intercept_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_contrast_intercept)
if not (i,j,k,l,m,n) in bc_r_sqr_dict:
bc_r_sqr_dict[(i,j,k,l,m,n)]=[]
bc_r_sqr_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_contrast_r_sqr)
if not (i,j,k,l,m,n) in auc_dict:
auc_dict[(i,j,k,l,m,n)]=[]
auc_dict[(i,j,k,l,m,n)].append(wta_report.roc.auc)
if not (i,j,k,l,m,n) in bfr_slope_dict:
bfr_slope_dict[(i,j,k,l,m,n)]=[]
bfr_slope_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_firing_rate_slope)
if not (i,j,k,l,m,n) in bfr_intercept_dict:
bfr_intercept_dict[(i,j,k,l,m,n)]=[]
bfr_intercept_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_firing_rate_intercept)
if not (i,j,k,l,m,n) in bfr_r_sqr_dict:
bfr_r_sqr_dict[(i,j,k,l,m,n)]=[]
bfr_r_sqr_dict[(i,j,k,l,m,n)].append(wta_report.bold.bold_firing_rate_r_sqr)
report_info.roc_auc[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.roc.auc
report_info.bc_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_contrast_slope
report_info.bc_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_contrast_intercept
report_info.bc_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_contrast_r_sqr
report_info.bfr_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_firing_rate_slope
report_info.bfr_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_firing_rate_intercept
report_info.bfr_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=wta_report.bold.bold_firing_rate_r_sqr
else:
report_info.roc_auc[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bc_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bfr_slope[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.bfr_intercept[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.brf_r_sqr[(p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e)]=0
report_info.num_groups=num_groups
report_info.trial_duration=trial_duration
report_info.num_trials=num_trials
report_info.p_b_e_range=p_b_e_range
report_info.p_x_e_range=p_x_e_range
report_info.p_e_e_range=p_e_e_range
report_info.p_e_i_range=p_e_i_range
report_info.p_i_i_range=p_i_i_range
report_info.p_i_e_range=p_i_e_range
summary_data.fill(auc_dict, bc_slope_dict, bc_intercept_dict, bc_r_sqr_dict, bfr_slope_dict, bfr_intercept_dict,
bfr_r_sqr_dict, smooth_missing_params=smooth_missing_params)
summary_data.write_to_file(os.path.join(base_report_dir,summary_filename))
bc_bayes_analysis=run_bayesian_analysis(summary_data.auc, summary_data.bc_slope, summary_data.bc_intercept,
summary_data.bc_r_sqr, num_trials, p_b_e_range, p_e_e_range, p_e_i_range, p_i_e_range, p_i_i_range, p_x_e_range)
bfr_bayes_analysis=run_bayesian_analysis(summary_data.auc, summary_data.bfr_slope, summary_data.bfr_intercept,
summary_data.bfr_r_sqr, num_trials, p_b_e_range, p_e_e_range, p_e_i_range, p_i_e_range, p_i_i_range, p_x_e_range)
bc_base_dir=os.path.join(base_report_dir, 'bold-contrast')
make_report_dirs(bc_base_dir)
render_summary_report(bc_base_dir, bc_bayes_analysis, p_b_e_range, p_e_e_range, p_e_i_range, p_i_e_range,
p_i_i_range, p_x_e_range, report_info)
bfr_base_dir=os.path.join(base_report_dir, 'bold-firing_rate')
make_report_dirs(bfr_base_dir)
render_summary_report(bfr_base_dir, bfr_bayes_analysis, p_b_e_range, p_e_e_range, p_e_i_range, p_i_e_range,
p_i_i_range, p_x_e_range, report_info)
def create_wta_network_report(file_prefix, contrast_range, num_trials, reports_dir, edesc, regenerate_network_plots=True,
regenerate_trial_plots=True):
make_report_dirs(reports_dir)
report_info=Struct()
report_info.edesc=edesc
report_info.trials=[]
(data_dir, data_file_prefix) = os.path.split(file_prefix)
total_trials=num_trials*len(contrast_range)
trial_contrast=np.zeros([total_trials,1])
trial_max_bold=np.zeros(total_trials)
trial_max_input=np.zeros([total_trials,1])
trial_max_rate=np.zeros([total_trials,1])
trial_rt=np.zeros([total_trials,1])
report_info.contrast_accuracy=np.zeros([len(contrast_range),1])
max_bold=[]
for j,contrast in enumerate(contrast_range):
contrast_max_bold=[]
report_info.contrast_accuracy[j]=0.0
for i in range(num_trials):
file_name='%s.contrast.%0.4f.trial.%d.h5' % (file_prefix, contrast, i)
print('opening %s' % file_name)
data=FileInfo(file_name)
if not i:
report_info.wta_params=data.wta_params
report_info.voxel_params=data.voxel_params
report_info.num_groups=data.num_groups
report_info.trial_duration=data.trial_duration
report_info.background_rate=data.background_rate
report_info.stim_start_time=data.stim_start_time
report_info.stim_end_time=data.stim_end_time
report_info.network_group_size=data.network_group_size
report_info.background_input_size=data.background_input_size
report_info.task_input_size=data.task_input_size
trial_idx=j*num_trials+i
trial = create_trial_report(data, reports_dir, contrast, i, regenerate_plots=regenerate_trial_plots)
trial_contrast[trial_idx]=trial.input_contrast
if not math.isnan(trial.max_bold):
trial_max_bold[trial_idx]=trial.max_bold
else:
if j>1 and max_bold[j-1]<1.0 and max_bold[j-2]<1.0:
trial_max_bold[trial_idx]=max_bold[j-1]+(max_bold[j-1]-max_bold[j-2])
elif j>0 and max_bold[j-1]<1.0:
trial_max_bold[trial_idx]=max_bold[j-1]*2.0
else:
trial_max_bold[trial_idx]=1.0
report_info.contrast_accuracy[j]+=trial.correct
trial_max_input[trial_idx]=trial.max_input
trial_max_rate[trial_idx]=trial.max_rate
trial_rt[trial_idx]=trial.rt
report_info.trials.append(trial)
contrast_max_bold.append(trial_max_bold[trial_idx])
report_info.contrast_accuracy[j]/=float(num_trials)
mean_contrast_bold=np.mean(np.array(contrast_max_bold))
max_bold.append(mean_contrast_bold)
clf=LinearRegression()
clf.fit(trial_max_input,trial_max_rate)
a=clf.coef_[0]
b=clf.intercept_
report_info.io_slope=a
report_info.io_intercept=b
report_info.io_r_sqr=clf.score(trial_max_input,trial_max_rate)
furl='img/input_output_rate.png'
fname=os.path.join(reports_dir, furl)
report_info.input_output_rate_url=furl
if regenerate_network_plots or not os.path.exists(fname):
fig=plt.figure()
plt.plot(trial_max_input, trial_max_rate, 'x')
x_min=np.min(trial_max_input)
x_max=np.max(trial_max_input)
plt.plot([x_min,x_max],[x_min,x_max],'--')
plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
plt.xlabel('Max Input Rate')
plt.ylabel('Max Population Rate')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/input_output_rate.eps'))
plt.close()
report_info.bold=create_bold_report(reports_dir, trial_contrast, trial_max_bold, trial_max_rate, trial_rt,
regenerate_plot=regenerate_network_plots)
report_info.roc=create_roc_report(file_prefix, report_info.num_groups, contrast_range, num_trials, reports_dir,
regenerate_plot=regenerate_network_plots)
#create report
template_file='wta_network_instance.html'
env = Environment(loader=FileSystemLoader(TEMPLATE_DIR))
template=env.get_template(template_file)
output_file='wta_network.%s.html' % data_file_prefix
fname=os.path.join(reports_dir,output_file)
stream=template.stream(rinfo=report_info)
stream.dump(fname)
return report_info
def create_summary_report(summary_file_name, base_report_dir, e_desc):
make_report_dirs(base_report_dir)
summary_data = SummaryData()
summary_data.read_from_file(summary_file_name)
report_info = Struct()
report_info.edesc = e_desc
report_info.roc_auc = {}
report_info.io_slope = {}
report_info.io_intercept = {}
report_info.io_r_sqr = {}
report_info.bc_slope = {}
report_info.bc_intercept = {}
report_info.bc_r_sqr = {}
report_info.bfr_slope = {}
report_info.bfr_intercept = {}
report_info.bfr_r_sqr = {}
for i, p_b_e in enumerate(summary_data.p_b_e_range):
for j, p_x_e in enumerate(summary_data.p_x_e_range):
for k, p_e_e in enumerate(summary_data.p_e_e_range):
for l, p_e_i in enumerate(summary_data.p_e_i_range):
for m, p_i_i in enumerate(summary_data.p_i_i_range):
for n, p_i_e in enumerate(summary_data.p_i_e_range):
if summary_data.auc[i, j, k, l, m, n] > 0:
report_info.roc_auc[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.auc[i, j, k, l, m,
n]
report_info.bc_slope[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bc_slope[i, j, k, l,
m, n]
report_info.bc_intercept[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bc_intercept[i, j,
k, l,
m, n]
report_info.bc_r_sqr[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bc_r_sqr[i, j, k, l,
m, n]
report_info.bfr_slope[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bfr_slope[i, j, k,
l, m, n]
report_info.bfr_intercept[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bfr_intercept[i, j,
k, l,
m, n]
report_info.bfr_r_sqr[(
p_b_e, p_x_e, p_e_e, p_e_i, p_i_i,
p_i_e)] = summary_data.bfr_r_sqr[i, j, k,
l, m, n]
else:
report_info.roc_auc[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i, p_i_e)] = 0
report_info.bc_slope[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i, p_i_e)] = 0
report_info.bc_intercept[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i,
p_i_e)] = 0
report_info.bc_r_sqr[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i, p_i_e)] = 0
report_info.bfr_slope[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i,
p_i_e)] = 0
report_info.bfr_intercept[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i,
p_i_e)] = 0
report_info.bfr_r_sqr[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i,
p_i_e)] = 0
report_info.num_groups = summary_data.num_groups
report_info.trial_duration = summary_data.trial_duration
report_info.num_trials = summary_data.num_trials
report_info.p_b_e_range = summary_data.p_b_e_range
report_info.p_x_e_range = summary_data.p_x_e_range
report_info.p_e_e_range = summary_data.p_e_e_range[:-1]
report_info.p_e_i_range = summary_data.p_e_i_range
report_info.p_i_i_range = summary_data.p_i_i_range
report_info.p_i_e_range = summary_data.p_i_e_range
bc_bayes_analysis = run_bayesian_analysis(
summary_data.auc, summary_data.bc_slope, summary_data.bc_intercept,
summary_data.bc_r_sqr, summary_data.num_trials,
summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range,
summary_data.p_i_i_range, summary_data.p_x_e_range)
bfr_bayes_analysis = run_bayesian_analysis(
summary_data.auc, summary_data.bfr_slope, summary_data.bfr_intercept,
summary_data.bfr_r_sqr, summary_data.num_trials,
summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range,
summary_data.p_i_i_range, summary_data.p_x_e_range)
bc_base_dir = os.path.join(base_report_dir, 'bold-contrast')
make_report_dirs(bc_base_dir)
render_summary_report(bc_base_dir, bc_bayes_analysis,
summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range,
summary_data.p_i_i_range, summary_data.p_x_e_range,
report_info)
bfr_base_dir = os.path.join(base_report_dir, 'bold-firing_rate')
make_report_dirs(bfr_base_dir)
render_summary_report(bfr_base_dir, bfr_bayes_analysis,
summary_data.p_b_e_range, summary_data.p_e_e_range,
summary_data.p_e_i_range, summary_data.p_i_e_range,
summary_data.p_i_i_range, summary_data.p_x_e_range,
report_info)