# Analyse data
# ------------
#
# First, we create an adequate design matrix with three columns: 'age',
# 'sex', 'intercept'.
import pandas as pd
import numpy as np
intercept = np.ones(n_subjects)
design_matrix = pd.DataFrame(np.vstack((age, sex, intercept)).T,
columns=['age', 'sex', 'intercept'])
#############################################################################
# Let's plot the design matrix.
from nilearn.plotting import plot_design_matrix
ax = plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')
##########################################################################
# Next, we specify and fit the second-level model when loading the data and
# also smooth a little bit to improve statistical behavior.
from nilearn.glm.second_level import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img)
second_level_model.fit(gray_matter_map_filenames,
design_matrix=design_matrix)
##########################################################################
# Estimating the contrast is very simple. We can just provide the column
# name of the design matrix.
raw.plot(duration=300, show_scrollbars=False)
# %%
# Create design matrix
# ------------------------------------
#
# Next we create a design matrix based on the annotation times in the simulated
# data. We use the nilearn plotting function to visualise the design matrix.
# For more details on this procedure see :ref:`tut-fnirs-hrf`.
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.0,
drift_order=1,
drift_model='polynomial')
fig, ax1 = plt.subplots(figsize=(10, 6), nrows=1, ncols=1)
fig = plot_design_matrix(design_matrix, ax=ax1)
# %%
# Estimate response on clean data
# -------------------------------
#
# Here we run the GLM analysis on the clean data.
# The design matrix had three columns, so we get an estimate for our simulated
# event, the first order drift, and the constant.
# We see that the estimate of the first component is 4e-6 (4 uM),
# which was the amplitude we used in the simulation.
# We also see that the mean square error of the model fit is close to zero.
glm_est = run_glm(raw, design_matrix)
glm_parameters['signal_scaling'] = standard_glm.scaling_axis ############################################################################## # Define the standard model # ------------------------- # Here, we create a basic :term:`GLM` for this run, which we can use to # highlight differences between the standard modeling approach and beta series # models. # We will just use the one created by # :func:`~nilearn.glm.first_level.first_level_from_bids`. standard_glm.fit(fmri_file, events_df) # The standard design matrix has one column for each condition, along with # columns for the confound regressors and drifts fig, ax = plt.subplots(figsize=(5, 10)) plotting.plot_design_matrix(standard_glm.design_matrices_[0], ax=ax) fig.show() ############################################################################## # Define the LSA model # -------------------- # We will now create a least squares- all (LSA) model. # This involves a simple transformation, where each trial of interest receives # its own unique trial type. # It's important to ensure that the original trial types can be inferred from # the updated trial-wise trial types, in order to collect the resulting # beta maps into condition-wise beta series. # Transform the DataFrame for LSA lsa_events_df = events_df.copy() conditions = lsa_events_df['trial_type'].unique()
graph.load_collections()
except ValueError:
graph.load_collections(scan_length=453) # TR = 1, nvols = 453
# %%
specs = root_node.run(group_by=root_node.group_by, force_dense=False)
len(specs)
# %%
api(specs[0])
# %%
specs[0]
# %%
plot_design_matrix(specs[0].X)
# %%
specs[0].entities
# %%
specs[0].metadata
# %%
bold = layout.get(**specs[0].entities, suffix='bold', extension='.nii.gz')[0]
# %%
# import nilearn.glm
# l1m = nilearn.glm.first_level.FirstLevelModel()
# l1m.fit(bold.get_image(), design_matrices=specs[0].X)
#########################################################################
# Finally we compute a FIR model
events = pd.DataFrame({
'trial_type': conditions,
'onset': onsets,
'duration': duration
})
hrf_model = 'FIR'
X3 = make_first_level_design_matrix(frame_times,
events,
hrf_model='fir',
drift_model='polynomial',
drift_order=3,
fir_delays=np.arange(1, 6))
#########################################################################
# Here are the three designs side by side.
from nilearn.plotting import plot_design_matrix
fig, (ax1, ax2, ax3) = plt.subplots(figsize=(10, 6), nrows=1, ncols=3)
plot_design_matrix(X1, ax=ax1)
ax1.set_title('Event-related design matrix', fontsize=12)
plot_design_matrix(X2, ax=ax2)
ax2.set_title('Block design matrix', fontsize=12)
plot_design_matrix(X3, ax=ax3)
ax3.set_title('FIR design matrix', fontsize=12)
#########################################################################
# Let's improve the layout and show the result.
plt.subplots_adjust(left=0.08, top=0.9, bottom=0.21, right=0.96, wspace=0.3)
plt.show()
##########################################################################
# We then assemble those into design matrices
unpaired_design_matrix = pd.DataFrame(condition_effect[:, np.newaxis],
columns=['vertical vs horizontal'])
paired_design_matrix = pd.DataFrame(
np.hstack((condition_effect[:, np.newaxis], subject_effect)),
columns=['vertical vs horizontal'] + subjects)
##########################################################################
# and plot the designs.
from nilearn.plotting import plot_design_matrix
_, (ax_unpaired,
ax_paired) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [1, 17]})
plot_design_matrix(unpaired_design_matrix, rescale=False, ax=ax_unpaired)
plot_design_matrix(paired_design_matrix, rescale=False, ax=ax_paired)
ax_unpaired.set_title('unpaired design', fontsize=12)
ax_paired.set_title('paired design', fontsize=12)
plt.tight_layout()
plotting.show()
##########################################################################
# We specify the analysis models and fit them.
from nilearn.glm.second_level import SecondLevelModel
second_level_model_unpaired = SecondLevelModel().fit(
second_level_input, design_matrix=unpaired_design_matrix)
second_level_model_paired = SecondLevelModel().fit(
second_level_input, design_matrix=paired_design_matrix)
high_pass=.01)
###############################################################################
# Now that we have specified the model, we can run it on the fMRI image
fmri_glm = fmri_glm.fit(fmri_img, events)
###############################################################################
# One can inspect the design matrix (rows represent time, and
# columns contain the predictors).
design_matrix = fmri_glm.design_matrices_[0]
###############################################################################
# Formally, we have taken the first design matrix, because the model is
# implictily meant to for multiple runs.
from nilearn.plotting import plot_design_matrix
plot_design_matrix(design_matrix)
import matplotlib.pyplot as plt
plt.show()
###############################################################################
# Save the design matrix image to disk
# first create a directory where you want to write the images
import os
outdir = 'results'
if not os.path.exists(outdir):
os.mkdir(outdir)
from os.path import join
plot_design_matrix(
design_matrix, output_file=join(outdir, 'design_matrix.png'))
def run_subject(sub, out_dir):
""" Runs pattern estimation for a single subject. """
print(f"INFO: Processing {sub}")
# Define in- and output directories
bids_dir = op.join(f'../{sub}')
fprep_dir = op.join(f'../derivatives/fmriprep/{sub}')
out_dir = op.join(out_dir, sub)
funcs = sorted(
glob(fprep_dir +
'/ses-?/func/*task-face*space-MNI*desc-preproc_bold.nii.gz'))
for func in funcs:
t_r = nib.load(func).header['pixdim'][4]
conf = func.split('_space')[0] + '_desc-confounds_timeseries.tsv'
mask = func.replace('preproc_bold', 'brain_mask')
events = bids_dir + func.split(fprep_dir)[1].split(
'_space')[0] + '_events.tsv'
flm = FirstLevelModel(t_r=t_r,
slice_time_ref=0.5,
hrf_model='glover',
drift_model='cosine',
high_pass=0.01,
mask_img=mask,
smoothing_fwhm=None,
noise_model='ols',
n_jobs=1,
minimize_memory=False)
# Select confounds
conf = pd.read_csv(conf, sep='\t').loc[:, [
'trans_x', 'trans_y', 'trans_z', 'rot_x', 'rot_y', 'rot_z', 'csf',
'white_matter'
]]
# Redefine output dir
this_out_dir = op.join(out_dir, func.split(fprep_dir)[1].split('/')[1])
for d in ['patterns', 'model', 'figures']:
if not op.isdir(op.join(this_out_dir, d)):
os.makedirs(op.join(this_out_dir, d), exist_ok=True)
# Fit model
flm.fit(run_imgs=func, events=events, confounds=conf)
# Save some stuff!
f_base = op.basename(func).split('preproc')[0]
rsq_out = op.join(this_out_dir, 'model', f_base + 'model_r2.nii.gz')
flm.r_square[0].to_filename(rsq_out)
dm = flm.design_matrices_[0]
dm_out = op.join(this_out_dir, 'model', f_base + 'design_matrix.tsv')
dm.to_csv(dm_out, sep='\t', index=False)
dmfig_out = op.join(this_out_dir, 'figures',
f_base + 'design_matrix.png')
plot_design_matrix(dm, output_file=dmfig_out)
dmcorrfig_out = op.join(this_out_dir, 'figures',
f_base + 'design_corr.png')
labels = dm.columns.tolist()[:-1]
ax = plot_design_matrix(dm.drop('constant', axis=1).corr())
ax.set_yticks(range(len(labels)))
ax.set_yticklabels(labels)
plt.savefig(dmcorrfig_out)
plt.close()
resids_out = op.join(this_out_dir, 'model',
f_base + 'model_residuals.nii.gz')
flm.residuals[0].to_filename(resids_out)
trials = [l for l in labels if 'STIM' in l]
b, vb = [], []
for trial in trials:
dat = flm.compute_contrast(trial, stat_type='t', output_type='all')
b.append(dat['effect_size'])
vb.append(dat['effect_variance'])
beta_out = op.join(this_out_dir, 'patterns',
f_base + 'trial_beta.nii.gz')
image.concat_imgs(b).to_filename(beta_out)
varbeta_out = op.join(this_out_dir, 'patterns',
f_base + 'trial_varbeta.nii.gz')
image.concat_imgs(vb).to_filename(varbeta_out)
def denoise(img_file,
tsv_file,
out_path,
col_names=False,
hp_filter=False,
lp_filter=False,
out_figure_path=False):
nii_ext = '.nii.gz'
FD_thr = [.5]
sc_range = np.arange(-1, 3)
constant = 'constant'
# read in files
img = load_niimg(img_file)
# get file info
img_name = os.path.basename(img.get_filename())
file_base = img_name[0:img_name.find('.')]
save_img_file = pjoin(out_path, file_base + \
'_NR' + nii_ext)
data = img.get_fdata()
df_orig = pandas.read_csv(tsv_file, '\t', na_values='n/a')
df = copy.deepcopy(df_orig)
Ntrs = df.values.shape[0]
print('# of TRs: ' + str(Ntrs))
assert (Ntrs == data.shape[len(data.shape) - 1])
# select columns to use as nuisance regressors
if col_names:
df = df[col_names]
str_append = ' [SELECTED regressors in CSV]'
else:
col_names = df.columns.tolist()
str_append = ' [ALL regressors in CSV]'
# fill in missing nuisance values with mean for that variable
for col in df.columns:
if sum(df[col].isnull()) > 0:
print('Filling in ' + str(sum(df[col].isnull())) +
' NaN value for ' + col)
df[col] = df[col].fillna(np.mean(df[col]))
print('# of Confound Regressors: ' + str(len(df.columns)) + str_append)
# implement HP filter in regression
TR = img.header.get_zooms()[-1]
frame_times = np.arange(Ntrs) * TR
if hp_filter:
hp_filter = float(hp_filter)
assert (hp_filter > 0)
df = make_first_level_design_matrix(frame_times,
high_pass=hp_filter,
add_regs=df.values,
add_reg_names=df.columns.tolist())
# fn adds intercept into dm
hp_cols = [col for col in df.columns if 'drift' in col]
print('# of High-pass Filter Regressors: ' + str(len(hp_cols)))
else:
# add in intercept column into data frame
df[constant] = 1
print('No High-pass Filter Applied')
dm = df.values
# prep data
data = np.reshape(data, (-1, Ntrs))
data_mean = np.mean(data, axis=1)
Nvox = len(data_mean)
# setup and run regression
model = regression.OLSModel(dm)
results = model.fit(data.T)
if not hp_filter:
results_orig_resid = copy.deepcopy(
results.residuals) # save for rsquared computation
# apply low-pass filter
if lp_filter:
# input to butterworth fn is time x voxels
low_pass = float(lp_filter)
Fs = 1. / TR
if low_pass >= Fs / 2:
raise ValueError(
'Low pass filter cutoff if too close to the Nyquist frequency (%s)'
% (Fs / 2))
temp_img_file = pjoin(out_path, file_base + \
'_temp' + nii_ext)
temp_img = nb.Nifti1Image(np.reshape(
results.residuals.T + np.reshape(data_mean, (Nvox, 1)),
img.shape).astype('float32'),
img.affine,
header=img.header)
temp_img.to_filename(temp_img_file)
results.residuals = butterworth(results.residuals,
sampling_rate=Fs,
low_pass=low_pass,
high_pass=None)
print('Low-pass Filter Applied: < ' + str(low_pass) + ' Hz')
# add mean back into data
clean_data = results.residuals.T + np.reshape(
data_mean, (Nvox, 1)) # add mean back into residuals
# save out new data file
print('Saving output file...')
clean_data = np.reshape(clean_data, img.shape).astype('float32')
new_img = nb.Nifti1Image(clean_data, img.affine, header=img.header)
new_img.to_filename(save_img_file)
######### generate Rsquared map for confounds only
if hp_filter:
# first remove low-frequency information from data
hp_cols.append(constant)
model_first = regression.OLSModel(df[hp_cols].values)
results_first = model_first.fit(data.T)
results_first_resid = copy.deepcopy(results_first.residuals)
del results_first, model_first
# compute sst - borrowed from matlab
sst = np.square(
np.linalg.norm(results_first_resid -
np.mean(results_first_resid, axis=0),
axis=0))
# now regress out 'true' confounds to estimate their Rsquared
nr_cols = [col for col in df.columns if 'drift' not in col]
model_second = regression.OLSModel(df[nr_cols].values)
results_second = model_second.fit(results_first_resid)
# compute sse - borrowed from matlab
sse = np.square(np.linalg.norm(results_second.residuals, axis=0))
del results_second, model_second, results_first_resid
elif not hp_filter:
# compute sst - borrowed from matlab
sst = np.square(
np.linalg.norm(data.T - np.mean(data.T, axis=0), axis=0))
# compute sse - borrowed from matlab
sse = np.square(np.linalg.norm(results_orig_resid, axis=0))
del results_orig_resid
# compute rsquared of nuisance regressors
zero_idx = np.logical_and(sst == 0, sse == 0)
sse[zero_idx] = 1
sst[zero_idx] = 1 # would be NaNs - become rsquared = 0
rsquare = 1 - np.true_divide(sse, sst)
rsquare[np.isnan(rsquare)] = 0
######### Visualizing DM & outputs
fontsize = 12
fontsize_title = 14
def_img_size = 8
if not out_figure_path:
out_figure_path = save_img_file[0:save_img_file.find('.')] + '_figures'
if not os.path.isdir(out_figure_path):
os.mkdir(out_figure_path)
png_append = '_' + img_name[0:img_name.find('.')] + '.png'
print('Output directory: ' + out_figure_path)
# DM corr matrix
cm = df[df.columns[0:-1]].corr()
curr_sz = copy.deepcopy(def_img_size)
if cm.shape[0] > def_img_size:
curr_sz = curr_sz + ((cm.shape[0] - curr_sz) * .3)
mtx_scale = curr_sz * 100
mask = np.zeros_like(cm, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
fig, ax = plt.subplots(figsize=(curr_sz, curr_sz))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(cm,
mask=mask,
cmap=cmap,
center=0,
vmax=cm[cm < 1].max().max(),
vmin=cm[cm < 1].min().min(),
square=True,
linewidths=.5,
cbar_kws={"shrink": .6})
ax.set_xticklabels(ax.get_xticklabels(),
rotation=60,
ha='right',
fontsize=fontsize)
ax.set_yticklabels(cm.columns.tolist(),
rotation=-30,
va='bottom',
fontsize=fontsize)
ax.set_title('Nuisance Corr. Matrix', fontsize=fontsize_title)
plt.tight_layout()
file_corr_matrix = 'Corr_matrix_regressors' + png_append
fig.savefig(pjoin(out_figure_path, file_corr_matrix))
plt.close(fig)
del fig, ax
# DM of Nuisance Regressors (all)
tr_label = 'TR (Volume #)'
fig, ax = plt.subplots(figsize=(curr_sz - 4.1, def_img_size))
x_scale_html = ((curr_sz - 4.1) / def_img_size) * 890
plotting.plot_design_matrix(df, ax=ax)
ax.set_title('Nuisance Design Matrix', fontsize=fontsize_title)
ax.set_xticklabels(ax.get_xticklabels(),
rotation=60,
ha='right',
fontsize=fontsize)
ax.set_yticklabels(ax.get_yticklabels(), fontsize=fontsize)
ax.set_ylabel(tr_label, fontsize=fontsize)
plt.tight_layout()
file_design_matrix = 'Design_matrix' + png_append
fig.savefig(pjoin(out_figure_path, file_design_matrix))
plt.close(fig)
del fig, ax
# FD timeseries plot
FD = 'FD'
poss_names = [
'FramewiseDisplacement', FD, 'framewisedisplacement', 'fd',
'framewise_displacement'
]
fd_idx = [df_orig.columns.__contains__(i) for i in poss_names]
if np.sum(fd_idx) > 0:
FD_name = np.array(poss_names)[fd_idx][0] #poss_names[fd_idx == True]
if sum(df_orig[FD_name].isnull()) > 0:
df_orig[FD_name] = df_orig[FD_name].fillna(
np.mean(df_orig[FD_name]))
y = df_orig[FD_name].values
Nremove = []
sc_idx = []
for thr_idx, thr in enumerate(FD_thr):
idx = y >= thr
sc_idx.append(copy.deepcopy(idx))
for iidx in np.where(idx)[0]:
for buffer in sc_range:
curr_idx = iidx + buffer
if curr_idx >= 0 and curr_idx <= len(idx):
sc_idx[thr_idx][curr_idx] = True
Nremove.append(np.sum(sc_idx[thr_idx]))
Nplots = len(FD_thr)
sns.set(font_scale=1.5)
sns.set_style('ticks')
fig, axes = plt.subplots(Nplots,
1,
figsize=(def_img_size * 1.5,
def_img_size / 2),
squeeze=False)
sns.despine()
bound = .4
fd_mean = np.mean(y)
for curr in np.arange(0, Nplots):
axes[curr, 0].plot(y)
axes[curr, 0].plot((-bound, Ntrs + bound),
FD_thr[curr] * np.ones((1, 2))[0],
'--',
color='black')
axes[curr, 0].scatter(np.arange(0, Ntrs), y, s=20)
if Nremove[curr] > 0:
info = scipy.ndimage.measurements.label(sc_idx[curr])
for cluster in np.arange(1, info[1] + 1):
temp = np.where(info[0] == cluster)[0]
axes[curr, 0].axvspan(temp.min() - bound,
temp.max() + bound,
alpha=.5,
color='red')
axes[curr, 0].set_ylabel('Framewise Disp. (' + FD + ')')
axes[curr, 0].set_title(FD + ': ' +
str(100 * Nremove[curr] / Ntrs)[0:4] +
'% of scan (' + str(Nremove[curr]) +
' volumes) would be scrubbed (FD thr.= ' +
str(FD_thr[curr]) + ')')
plt.text(Ntrs + 1,
FD_thr[curr] - .01,
FD + ' = ' + str(FD_thr[curr]),
fontsize=fontsize)
plt.text(Ntrs,
fd_mean - .01,
'avg = ' + str(fd_mean),
fontsize=fontsize)
axes[curr, 0].set_xlim((-bound, Ntrs + 8))
plt.tight_layout()
axes[curr, 0].set_xlabel(tr_label)
file_fd_plot = FD + '_timeseries' + png_append
fig.savefig(pjoin(out_figure_path, file_fd_plot))
plt.close(fig)
del fig, axes
print(FD + ' timeseries plot saved')
else:
print(FD + ' not found: ' + FD + ' timeseries not plotted')
file_fd_plot = None
# Carpet and DVARS plots - before & after nuisance regression
# need to create mask file to input to DVARS function
mask_file = pjoin(out_figure_path, 'mask_temp.nii.gz')
nifti_masker = NiftiMasker(mask_strategy='epi', standardize=False)
nifti_masker.fit(img)
nifti_masker.mask_img_.to_filename(mask_file)
# create 2 or 3 carpet plots, depending on if LP filter is also applied
Ncarpet = 2
total_sz = int(16)
carpet_scale = 840
y_labels = ['Input (voxels)', 'Output \'cleaned\'']
imgs = [img, new_img]
img_files = [img_file, save_img_file]
color = ['red', 'salmon']
labels = ['input', 'cleaned']
if lp_filter:
Ncarpet = 3
total_sz = int(20)
carpet_scale = carpet_scale * (9 / 8)
y_labels = ['Input', 'Clean Pre-LP', 'Clean LP']
imgs.insert(1, temp_img)
img_files.insert(1, temp_img_file)
color.insert(1, 'firebrick')
labels.insert(1, 'clean pre-LP')
labels[-1] = 'clean LP'
dvars = []
print('Computing dvars...')
for in_file in img_files:
temp = nac.compute_dvars(in_file=in_file, in_mask=mask_file)[1]
dvars.append(np.hstack((temp.mean(), temp)))
del temp
small_sz = 2
fig = plt.figure(figsize=(def_img_size * 1.5,
def_img_size + ((Ncarpet - 2) * 1)))
row_used = 0
if np.sum(fd_idx) > 0: # if FD data is available
row_used = row_used + small_sz
ax0 = plt.subplot2grid((total_sz, 1), (0, 0), rowspan=small_sz)
ax0.plot(y)
ax0.scatter(np.arange(0, Ntrs), y, s=10)
curr = 0
if Nremove[curr] > 0:
info = scipy.ndimage.measurements.label(sc_idx[curr])
for cluster in np.arange(1, info[1] + 1):
temp = np.where(info[0] == cluster)[0]
ax0.axvspan(temp.min() - bound,
temp.max() + bound,
alpha=.5,
color='red')
ax0.set_ylabel(FD)
for side in ["top", "right", "bottom"]:
ax0.spines[side].set_color('none')
ax0.spines[side].set_visible(False)
ax0.set_xticks([])
ax0.set_xlim((-.5, Ntrs - .5))
ax0.spines["left"].set_position(('outward', 10))
ax_d = plt.subplot2grid((total_sz, 1), (row_used, 0), rowspan=small_sz)
for iplot in np.arange(len(dvars)):
ax_d.plot(dvars[iplot], color=color[iplot], label=labels[iplot])
ax_d.set_ylabel('DVARS')
for side in ["top", "right", "bottom"]:
ax_d.spines[side].set_color('none')
ax_d.spines[side].set_visible(False)
ax_d.set_xticks([])
ax_d.set_xlim((-.5, Ntrs - .5))
ax_d.spines["left"].set_position(('outward', 10))
ax_d.legend(fontsize=fontsize - 2)
row_used = row_used + small_sz
st = 0
carpet_each = int((total_sz - row_used) / Ncarpet)
for idx, img_curr in enumerate(imgs):
ax_curr = plt.subplot2grid((total_sz, 1), (row_used + st, 0),
rowspan=carpet_each)
fig = plotting.plot_carpet(img_curr, figure=fig, axes=ax_curr)
ax_curr.set_ylabel(y_labels[idx])
for side in ["bottom", "left"]:
ax_curr.spines[side].set_position(('outward', 10))
if idx < len(imgs) - 1:
ax_curr.spines["bottom"].set_visible(False)
ax_curr.set_xticklabels('')
ax_curr.set_xlabel('')
st = st + carpet_each
file_carpet_plot = 'Carpet_plots' + png_append
fig.savefig(pjoin(out_figure_path, file_carpet_plot))
plt.close()
del fig, ax0, ax_curr, ax_d, dvars
os.remove(mask_file)
print('Carpet/DVARS plots saved')
if lp_filter:
os.remove(temp_img_file)
del temp_img
# Display T-stat maps for nuisance regressors
# create mean img
img_size = (img.shape[0], img.shape[1], img.shape[2])
mean_img = nb.Nifti1Image(np.reshape(data_mean, img_size), img.affine)
mx = []
for idx, col in enumerate(df.columns):
if not 'drift' in col and not constant in col:
con_vector = np.zeros((1, df.shape[1]))
con_vector[0, idx] = 1
con = results.Tcontrast(con_vector)
mx.append(np.max(np.absolute([con.t.min(), con.t.max()])))
mx = .8 * np.max(mx)
t_png = 'Tstat_'
file_tstat = []
for idx, col in enumerate(df.columns):
if not 'drift' in col and not constant in col:
con_vector = np.zeros((1, df.shape[1]))
con_vector[0, idx] = 1
con = results.Tcontrast(con_vector)
m_img = nb.Nifti1Image(np.reshape(con, img_size), img.affine)
title_str = col + ' Tstat'
fig = plotting.plot_stat_map(m_img,
mean_img,
threshold=3,
colorbar=True,
display_mode='z',
vmax=mx,
title=title_str,
cut_coords=7)
file_temp = t_png + col + png_append
fig.savefig(pjoin(out_figure_path, file_temp))
file_tstat.append({'name': col, 'file': file_temp})
plt.close()
del fig, file_temp
print(title_str + ' map saved')
# Display R-sq map for nuisance regressors
m_img = nb.Nifti1Image(np.reshape(rsquare, img_size), img.affine)
title_str = 'Nuisance Rsq'
mx = .95 * rsquare.max()
fig = plotting.plot_stat_map(m_img,
mean_img,
threshold=.2,
colorbar=True,
display_mode='z',
vmax=mx,
title=title_str,
cut_coords=7)
file_rsq_map = 'Rsquared' + png_append
fig.savefig(pjoin(out_figure_path, file_rsq_map))
plt.close()
del fig
print(title_str + ' map saved')
######### html report
templateLoader = jinja2.FileSystemLoader(searchpath="/")
templateEnv = jinja2.Environment(loader=templateLoader)
templateVars = {
"img_file": img_file,
"save_img_file": save_img_file,
"Ntrs": Ntrs,
"tsv_file": tsv_file,
"col_names": col_names,
"hp_filter": hp_filter,
"lp_filter": lp_filter,
"file_design_matrix": file_design_matrix,
"file_corr_matrix": file_corr_matrix,
"file_fd_plot": file_fd_plot,
"file_rsq_map": file_rsq_map,
"file_tstat": file_tstat,
"x_scale": x_scale_html,
"mtx_scale": mtx_scale,
"file_carpet_plot": file_carpet_plot,
"carpet_scale": carpet_scale
}
TEMPLATE_FILE = pjoin(os.getcwd(), "report_template.html")
template = templateEnv.get_template(TEMPLATE_FILE)
outputText = template.render(templateVars)
html_file = pjoin(out_figure_path,
img_name[0:img_name.find('.')] + '.html')
with open(html_file, "w") as f:
f.write(outputText)
print('')
print('HTML report: ' + html_file)
return new_img
# First, we create an adequate design matrix with three columns: 'age', 'sex',
# and 'intercept'.
import numpy as np
import pandas as pd
intercept = np.ones(n_subjects)
design_matrix = pd.DataFrame(
np.vstack((age, sex, intercept)).T,
columns=['age', 'sex', 'intercept'],
)
###############################################################################
# Let's plot the design matrix.
from nilearn import plotting
ax = plotting.plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')
###############################################################################
# Next, we specify and fit the second-level model when loading the data and
# also smooth a little bit to improve statistical behavior.
from nilearn.glm.second_level import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img)
second_level_model.fit(
gray_matter_map_filenames,
design_matrix=design_matrix,
)
###############################################################################
