def plot_bold_contrast(data_dir, num_groups, trial_duration, num_trials, p_b_e,
p_x_e, p_e_e, p_e_i, p_i_i, p_i_e):
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' %\
(num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e)
file_prefix = os.path.join(data_dir, file_desc)
trial_contrast = np.zeros([num_trials, 1])
trial_max_bold = np.zeros(num_trials)
trial_max_bold_exc = np.zeros(num_trials)
for i in range(num_trials):
file_name = '%s.trial.%d.h5' % (file_prefix, i)
print('opening %s' % file_name)
data = FileInfo(file_name)
trial_contrast[i] = abs(data.input_freq[0] - data.input_freq[1]) / sum(
data.input_freq)
trial_max_bold[i] = np.max(data.voxel_rec['y'][0])
trial_max_bold_exc[i] = np.max(data.voxel_exc_rec['y'][0])
clf = LinearRegression()
clf.fit(trial_contrast, trial_max_bold)
a = clf.coef_[0]
b = clf.intercept_
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')
plt.show()
clf = LinearRegression()
clf.fit(trial_contrast, trial_max_bold_exc)
a = clf.coef_[0]
b = clf.intercept_
fig = plt.figure()
plt.plot(trial_contrast, trial_max_bold_exc, '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')
plt.title('Exc only')
plt.show()
def test_ridge_vs_lstsq():
"""On alpha=0., Ridge and OLS yield the same solution."""
# we need more samples than features
n_samples, n_features = 5, 4
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=0.)
ols = LinearRegression()
ridge.fit(X, y)
ols.fit (X, y)
assert_almost_equal(ridge.coef_, ols.coef_)
ridge.fit(X, y, fit_intercept=False)
ols.fit (X, y, fit_intercept=False)
assert_almost_equal(ridge.coef_, ols.coef_)
def test_ridge_vs_lstsq():
"""On alpha=0., Ridge and OLS yield the same solution."""
# we need more samples than features
n_samples, n_features = 5, 4
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
ridge = Ridge(alpha=0.)
ols = LinearRegression()
ridge.fit(X, y)
ols.fit(X, y)
assert_almost_equal(ridge.coef_, ols.coef_)
ridge.fit(X, y, fit_intercept=False)
ols.fit(X, y, fit_intercept=False)
assert_almost_equal(ridge.coef_, ols.coef_)
def plot_bold_contrast(data_dir, num_groups, trial_duration, num_trials, p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e):
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' %\
(num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e)
file_prefix=os.path.join(data_dir,file_desc)
trial_contrast=np.zeros([num_trials,1])
trial_max_bold=np.zeros(num_trials)
trial_max_bold_exc=np.zeros(num_trials)
for i in range(num_trials):
file_name='%s.trial.%d.h5' % (file_prefix, i)
print('opening %s' % file_name)
data=FileInfo(file_name)
trial_contrast[i]=abs(data.input_freq[0]-data.input_freq[1])/sum(data.input_freq)
trial_max_bold[i]=np.max(data.voxel_rec['y'][0])
trial_max_bold_exc[i]=np.max(data.voxel_exc_rec['y'][0])
clf=LinearRegression()
clf.fit(trial_contrast,trial_max_bold)
a=clf.coef_[0]
b=clf.intercept_
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')
plt.show()
clf=LinearRegression()
clf.fit(trial_contrast,trial_max_bold_exc)
a=clf.coef_[0]
b=clf.intercept_
fig=plt.figure()
plt.plot(trial_contrast, trial_max_bold_exc, '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')
plt.title('Exc only')
plt.show()
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_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