def create_roc_report(file_prefix,
num_groups,
contrast_range,
num_trials,
reports_dir,
regenerate_plot=True):
num_extra_trials = 10
roc_report = Struct()
roc_report.auc = get_auc(file_prefix, contrast_range, num_trials,
num_extra_trials, num_groups)
roc_report.auc_single_option = []
roc_url = 'img/roc.png'
fname = os.path.join(reports_dir, roc_url)
roc_report.roc_url = roc_url
if regenerate_plot or not os.path.exists(fname):
fig = plt.figure()
for i in range(num_groups):
roc = get_roc_single_option(file_prefix, contrast_range,
num_trials, num_extra_trials, i)
plt.plot(roc[:, 0], roc[:, 1], 'x-', label='option %d' % i)
roc_report.auc_single_option.append(
get_auc_single_option(file_prefix, contrast_range, num_trials,
num_extra_trials, i))
plt.plot([0, 1], [0, 1], '--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/roc.eps'))
plt.close()
return roc_report
def create_trial_report(data,
reports_dir,
contrast,
trial_idx,
regenerate_plots=True):
trial = Struct()
trial.input_freq = data.input_freq
trial.input_contrast = abs(data.input_freq[0] - data.input_freq[1]) / sum(
data.input_freq)
trial.correct = 0.0
option_idx = -1
if data.input_freq[0] > data.input_freq[1]:
option_idx = 0
elif data.input_freq[1] > data.input_freq[0]:
option_idx = 1
if option_idx > -1:
if np.max(data.e_firing_rates[option_idx, 6500:7500]) > np.max(
data.e_firing_rates[1 - option_idx, 6500:7500]):
trial.correct = 1.0
trial.rt = data.rt
max_input_idx = np.where(
trial.input_freq == np.max(trial.input_freq))[0][0]
trial.max_input = trial.input_freq[max_input_idx]
trial.max_rate = np.max(data.e_firing_rates[max_input_idx])
trial.e_raster_url = None
trial.i_raster_url = None
if data.e_spike_neurons is not None and data.i_spike_neurons is not None:
furl = 'img/e_raster.contrast.%0.4f.trial.%d.png' % (contrast,
trial_idx)
fname = os.path.join(reports_dir, furl)
trial.e_raster_url = furl
if regenerate_plots or not os.path.exists(fname):
e_group_sizes = [
int(4 * data.network_group_size / 5)
for i in range(data.num_groups)
]
fig = plot_raster(data.e_spike_neurons, data.e_spike_times,
e_group_sizes)
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir, 'img/e_raster.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
furl = 'img/i_raster.contrast.%0.4f.trial.%d.png' % (contrast,
trial_idx)
fname = os.path.join(reports_dir, furl)
trial.i_raster_url = furl
if regenerate_plots or not os.path.exists(fname):
i_group_sizes = [
int(data.network_group_size / 5)
for i in range(data.num_groups)
]
fig = plot_raster(data.i_spike_neurons, data.i_spike_times,
i_group_sizes)
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir, 'img/i_raster.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
trial.firing_rate_url = None
if data.e_firing_rates is not None and data.i_firing_rates is not None:
furl = 'img/firing_rate.contrast.%0.4f.trial.%d.png' % (contrast,
trial_idx)
fname = os.path.join(reports_dir, furl)
trial.firing_rate_url = furl
if regenerate_plots or not os.path.exists(fname):
fig = plt.figure()
ax = plt.subplot(211)
for i, pop_rate in enumerate(data.e_firing_rates):
ax.plot(np.array(range(len(pop_rate))) * .1,
pop_rate / Hz,
label='group %d' % i)
plt.xlabel('Time (ms)')
plt.ylabel('Firing Rate (Hz)')
ax = plt.subplot(212)
for i, pop_rate in enumerate(data.i_firing_rates):
ax.plot(np.array(range(len(pop_rate))) * .1,
pop_rate / Hz,
label='group %d' % i)
plt.xlabel('Time (ms)')
plt.ylabel('Firing Rate (Hz)')
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir,
'img/firing_rate.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
trial.neural_state_url = None
if data.neural_state_rec is not None:
furl = 'img/neural_state.contrast.%0.4f.trial.%d.png' % (contrast,
trial_idx)
fname = os.path.join(reports_dir, furl)
trial.neural_state_url = furl
if regenerate_plots or not os.path.exists(fname):
fig = plt.figure()
for i in range(data.num_groups):
times = np.array(
range(len(data.neural_state_rec['g_ampa_r'][i * 2]))) * .1
ax = plt.subplot(data.num_groups * 100 + 20 + (i * 2 + 1))
ax.plot(times,
data.neural_state_rec['g_ampa_r'][i * 2] / nA,
label='AMPA-recurrent')
ax.plot(times,
data.neural_state_rec['g_ampa_x'][i * 2] / nA,
label='AMPA-task')
ax.plot(times,
data.neural_state_rec['g_ampa_b'][i * 2] / nA,
label='AMPA-backgrnd')
ax.plot(times,
data.neural_state_rec['g_nmda'][i * 2] / nA,
label='NMDA')
ax.plot(times,
data.neural_state_rec['g_gaba_a'][i * 2] / nA,
label='GABA_A')
plt.xlabel('Time (ms)')
plt.ylabel('Conductance (nA)')
ax = plt.subplot(data.num_groups * 100 + 20 + (i * 2 + 2))
ax.plot(times,
data.neural_state_rec['g_ampa_r'][i * 2 + 1] / nA,
label='AMPA-recurrent')
ax.plot(times,
data.neural_state_rec['g_ampa_x'][i * 2 + 1] / nA,
label='AMPA-task')
ax.plot(times,
data.neural_state_rec['g_ampa_b'][i * 2 + 1] / nA,
label='AMPA-backgrnd')
ax.plot(times,
data.neural_state_rec['g_nmda'][i * 2 + 1] / nA,
label='NMDA')
ax.plot(times,
data.neural_state_rec['g_gaba_a'][i * 2 + 1] / nA,
label='GABA_A')
plt.xlabel('Time (ms)')
plt.ylabel('Conductance (nA)')
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir,
'img/neural_state.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
trial.lfp_url = None
if data.lfp_rec is not None:
furl = 'img/lfp.contrast.%0.4f.trial.%d.png' % (contrast, trial_idx)
fname = os.path.join(reports_dir, furl)
trial.lfp_url = furl
if regenerate_plots or not os.path.exists(fname):
fig = plt.figure()
ax = plt.subplot(111)
lfp = get_lfp_signal(data)
ax.plot(np.array(range(len(lfp))), lfp / mA)
plt.xlabel('Time (ms)')
plt.ylabel('LFP (mA)')
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir, 'img/lfp.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
trial.voxel_url = None
trial.max_bold = 0
if data.voxel_rec is not None:
trial.max_bold = -1000
for val in data.voxel_rec['y'][0]:
if not math.isnan(val) and val > trial.max_bold:
trial.max_bold = val
furl = 'img/voxel.contrast.%0.4f.trial.%d.png' % (contrast, trial_idx)
fname = os.path.join(reports_dir, furl)
trial.voxel_url = furl
if regenerate_plots or not os.path.exists(fname):
end_idx = int(data.trial_duration / ms / .1)
fig = plt.figure()
ax = plt.subplot(211)
ax.plot(
np.array(range(end_idx)) * .1,
data.voxel_rec['G_total'][0][:end_idx] / nA)
plt.xlabel('Time (ms)')
plt.ylabel('Total Synaptic Activity (nA)')
ax = plt.subplot(212)
ax.plot(
np.array(range(len(data.voxel_rec['y'][0]))) * .1 * ms,
data.voxel_rec['y'][0])
plt.xlabel('Time (s)')
plt.ylabel('BOLD')
save_to_png(fig, fname)
save_to_eps(
fig,
os.path.join(
reports_dir, 'img/voxel.contrast.%0.4f.trial.%d.eps' %
(contrast, trial_idx)))
plt.close()
return trial
def create_bold_report(reports_dir,
trial_contrast,
trial_max_bold,
trial_max_rate,
trial_rt,
regenerate_plot=True):
report_info = Struct()
clf = LinearRegression()
clf.fit(trial_contrast, trial_max_bold)
a = clf.coef_[0]
b = clf.intercept_
report_info.bold_contrast_slope = a
report_info.bold_contrast_intercept = b
report_info.bold_contrast_r_sqr = clf.score(trial_contrast, trial_max_bold)
furl = 'img/contrast_bold.png'
fname = os.path.join(reports_dir, furl)
report_info.contrast_bold_url = furl
if regenerate_plot or not os.path.exists(fname):
fig = plt.figure()
plt.plot(trial_contrast, trial_max_bold, 'x')
x_min = np.min(trial_contrast)
x_max = np.max(trial_contrast)
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/contrast_bold.eps'))
plt.close()
clf = LinearRegression()
clf.fit(trial_max_rate, trial_max_bold)
a = clf.coef_[0]
b = clf.intercept_
report_info.bold_firing_rate_slope = a
report_info.bold_firing_rate_intercept = b
report_info.bold_firing_rate_r_sqr = clf.score(trial_max_rate,
trial_max_bold)
furl = 'img/firing_rate_bold.png'
fname = os.path.join(reports_dir, furl)
report_info.firing_rate_bold_url = furl
if regenerate_plot or not os.path.exists(fname):
fig = plt.figure()
plt.plot(trial_max_rate, trial_max_bold, 'x')
x_min = np.min(trial_max_rate)
x_max = np.max(trial_max_rate)
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Max Firing Rate')
plt.ylabel('Max BOLD')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir, 'img/firing_rate_bold.eps'))
plt.close()
clf = LinearRegression()
clf.fit(trial_rt, trial_max_bold)
a = clf.coef_[0]
b = clf.intercept_
report_info.bold_rt_slope = a
report_info.bold_rt_intercept = b
report_info.bold_rt_r_sqr = clf.score(trial_rt, trial_max_bold)
furl = 'img/response_time_bold.png'
fname = os.path.join(reports_dir, furl)
report_info.response_time_bold_url = furl
if regenerate_plot or not os.path.exists(fname):
fig = plt.figure()
plt.plot(trial_rt, trial_max_bold, 'x')
x_min = np.min(trial_rt)
x_max = np.max(trial_rt)
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Response Time')
plt.ylabel('Max BOLD')
save_to_png(fig, fname)
save_to_eps(fig, os.path.join(reports_dir,
'img/response_time_bold.eps'))
plt.close()
return 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_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_bayesian_report(title, num_groups, trial_duration, roc_auc,
bc_slope, bc_intercept, bc_r_sqr, evidence,
posterior, marginals, p_b_e_range, p_x_e_range,
p_e_e_range, p_e_i_range, p_i_i_range, p_i_e_range,
file_prefix, reports_dir, edesc, marginal_ylim):
report_info = Struct()
report_info.title = title
report_info.edesc = edesc
report_info.evidence = evidence
report_info.roc_auc = roc_auc
report_info.bc_slope = bc_slope
report_info.bc_intercept = bc_intercept
report_info.bc_r_sqr = bc_r_sqr
report_info.num_groups = num_groups
report_info.trial_duration = trial_duration
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
report_info.posterior = {}
for i, p_b_e in enumerate(p_b_e_range):
for j, p_x_e in enumerate(p_x_e_range):
for k, p_e_e in enumerate(p_e_e_range):
for l, p_e_i in enumerate(p_e_i_range):
for m, p_i_i in enumerate(p_i_i_range):
for n, p_i_e in enumerate(p_i_e_range):
if posterior[i, j, k, l, m, n] > 0:
report_info.posterior[(p_b_e, p_x_e, p_e_e,
p_e_i, p_i_i,
p_i_e)] = posterior[i,
j,
k,
l,
m,
n]
report_info.marginal_prior_p_b_e_url,\
report_info.marginal_likelihood_p_b_e_url,\
report_info.marginal_posterior_p_b_e_url=render_marginal_report('p_b_e', p_b_e_range,
marginals.prior_p_b_e, marginals.likelihood_p_b_e, marginals.posterior_p_b_e, file_prefix, reports_dir, marginal_ylim)
report_info.marginal_prior_p_x_e_url,\
report_info.marginal_likelihood_p_x_e_url,\
report_info.marginal_posterior_p_x_e_url=render_marginal_report('p_x_e', p_x_e_range,
marginals.prior_p_x_e, marginals.likelihood_p_x_e, marginals.posterior_p_x_e, file_prefix, reports_dir, marginal_ylim)
report_info.marginal_prior_p_e_e_url,\
report_info.marginal_likelihood_p_e_e_url,\
report_info.marginal_posterior_p_e_e_url=render_marginal_report('p_e_e', p_e_e_range,
marginals.prior_p_e_e, marginals.likelihood_p_e_e, marginals.posterior_p_e_e, file_prefix, reports_dir, marginal_ylim)
report_info.marginal_prior_p_e_i_url,\
report_info.marginal_likelihood_p_e_i_url,\
report_info.marginal_posterior_p_e_i_url=render_marginal_report('p_e_i', p_e_i_range,
marginals.prior_p_e_i, marginals.likelihood_p_e_i, marginals.posterior_p_e_i, file_prefix, reports_dir, marginal_ylim)
report_info.marginal_prior_p_i_i_url,\
report_info.marginal_likelihood_p_i_i_url,\
report_info.marginal_posterior_p_i_i_url=render_marginal_report('p_i_i', p_i_i_range,
marginals.prior_p_i_i, marginals.likelihood_p_i_i, marginals.posterior_p_i_i, file_prefix, reports_dir, marginal_ylim)
report_info.marginal_prior_p_i_e_url,\
report_info.marginal_likelihood_p_i_e_url,\
report_info.marginal_posterior_p_i_e_url=render_marginal_report('p_i_e', p_i_e_range,
marginals.prior_p_i_e, marginals.likelihood_p_i_e, marginals.posterior_p_i_e, file_prefix, reports_dir, marginal_ylim)
report_info.joint_marginal_p_b_e_p_x_e_url = render_joint_marginal_report(
'p_b_e', 'p_x_e', p_b_e_range, p_x_e_range,
marginals.posterior_p_b_e_p_x_e, file_prefix, reports_dir)
report_info.joint_marginal_p_e_e_p_e_i_url = render_joint_marginal_report(
'p_e_e', 'p_e_i', p_e_e_range, p_e_i_range,
marginals.posterior_p_e_e_p_e_i, file_prefix, reports_dir)
report_info.joint_marginal_p_e_e_p_i_i_url = render_joint_marginal_report(
'p_e_e', 'p_i_i', p_e_e_range, p_i_i_range,
marginals.posterior_p_e_e_p_i_i, file_prefix, reports_dir)
report_info.joint_marginal_p_e_e_p_i_e_url = render_joint_marginal_report(
'p_e_e', 'p_i_e', p_e_e_range, p_i_e_range,
marginals.posterior_p_e_e_p_i_e, file_prefix, reports_dir)
report_info.joint_marginal_p_e_i_p_i_i_url = render_joint_marginal_report(
'p_e_i', 'p_i_i', p_e_i_range, p_i_i_range,
marginals.posterior_p_e_i_p_i_i, file_prefix, reports_dir)
report_info.joint_marginal_p_e_i_p_i_e_url = render_joint_marginal_report(
'p_e_i', 'p_i_e', p_e_i_range, p_i_e_range,
marginals.posterior_p_e_i_p_i_e, file_prefix, reports_dir)
report_info.joint_marginal_p_i_i_p_i_e_url = render_joint_marginal_report(
'p_i_i', 'p_i_e', p_i_i_range, p_i_e_range,
marginals.posterior_p_i_i_p_i_e, file_prefix, reports_dir)
return report_info
