def test_show_contrast_matrix():
# test that the show code indeed (formally) runs
frame_times = np.linspace(0, 127 * 1., 128)
dmtx = make_first_level_design_matrix(
frame_times, drift_model='polynomial', drift_order=3)
contrast = np.ones(4)
ax = plot_contrast_matrix(contrast, dmtx)
assert (ax is not None)
with InTemporaryDirectory():
ax = plot_contrast_matrix(contrast, dmtx, output_file='contrast.png')
assert os.path.exists('contrast.png')
assert (ax is None)
plot_contrast_matrix(contrast, dmtx, output_file='contrast.pdf')
assert os.path.exists('contrast.pdf')
def _plot_contrasts(contrasts, design_matrices):
"""
Accepts dict of contrasts and list of design matrices and generates
a dict of contrast titles & HTML for SVG Image data url
for corresponding contrast plot.
Parameters
----------
contrasts: Dict[str, np.array or str]
Contrast information, as a dict
{'contrast_title_1, contrast_info_1/title_1, ...}
design_matrices: List[pd.Dataframe]
Design matrices computed in the model.
Returns
-------
contrast_plots: Dict[str, svg img]
Dict of contrast title and svg image data url
for corresponding contrast plot.
"""
all_contrasts_plots = {}
contrast_template_path = os.path.join(HTML_TEMPLATE_ROOT_PATH,
'contrast_template.html')
with open(contrast_template_path) as html_template_obj:
contrast_template_text = html_template_obj.read()
for design_matrix in design_matrices:
for contrast_name, contrast_data in contrasts.items():
contrast_text_ = string.Template(contrast_template_text)
contrast_plot = plot_contrast_matrix(contrast_data,
design_matrix,
colorbar=True)
contrast_plot.set_xlabel(contrast_name)
contrast_plot.figure.set_figheight(2)
contrast_plot.figure.set_tight_layout(True)
url_contrast_plot_svg = plot_to_svg(contrast_plot)
# prevents sphinx-gallery & jupyter
# from scraping & inserting plots
plt.close()
contrasts_for_subsitution = {
'contrast_plot': url_contrast_plot_svg,
'contrast_name': contrast_name,
}
contrast_text_ = contrast_text_.safe_substitute(
contrasts_for_subsitution)
all_contrasts_plots[contrast_name] = contrast_text_
return all_contrasts_plots
contrasts = {
'left-right': (contrasts['visual_left_hand_button_press'] +
contrasts['audio_left_hand_button_press'] -
contrasts['visual_right_hand_button_press'] -
contrasts['audio_right_hand_button_press']),
'H-V': (contrasts['horizontal_checkerboard'] -
contrasts['vertical_checkerboard']),
'audio-video':
contrasts['audio'] - contrasts['video'],
'sentences-computation':
(contrasts['sentences'] - contrasts['computation'])
}
#########################################################################
# Take a look at the contrasts.
plot_contrast_matrix(contrasts['left-right'], design_matrix)
#########################################################################
# Take a breath.
#
# We can now proceed by estimating the contrasts and displaying them.
import matplotlib.pyplot as plt
from nilearn.plotting import plot_stat_map
fig = plt.figure(figsize=(11, 3))
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
ax = plt.subplot(1, len(contrasts), 1 + index)
z_map = first_level_model.compute_contrast(contrast_val,
output_type='z_score')
plot_stat_map(z_map,
from nilearn.reporting import compare_niimgs
compare_niimgs([z_map], [fsl_z_map],
model.masker_,
ref_label='Nilearn',
src_label='FSL')
plt.show()
#############################################################################
# Simple statistical report of thresholded contrast
# -----------------------------------------------------
# We display the contrast plot and table with cluster information
from nilearn.reporting import plot_contrast_matrix
plot_contrast_matrix('StopSuccess - Go', design_matrix)
plotting.plot_glass_brain(z_map,
colorbar=True,
threshold=norm.isf(0.001),
plot_abs=False,
display_mode='z',
figure=plt.figure(figsize=(4, 4)))
plt.show()
###############################################################################
# We can get a latex table from a Pandas Dataframe for display and publication purposes
from nilearn.reporting import get_clusters_table
print(get_clusters_table(z_map, norm.isf(0.001), 10).to_latex())
#########################################################################
'rest': array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0.]),
}
###############################################################################
# We can then compare the two conditions 'active' and 'rest' by
# defining the corresponding contrast:
active_minus_rest = conditions['active'] - conditions['rest']
###############################################################################
# Let's look at it: plot the coefficients of the contrast, indexed by
# the names of the columns of the design matrix.
from nilearn.reporting import plot_contrast_matrix
plot_contrast_matrix(active_minus_rest, design_matrix=design_matrix)
###############################################################################
# Below, we compute the estimated effect. It is in BOLD signal unit,
# but has no statistical guarantees, because it does not take into
# account the associated variance.
eff_map = fmri_glm.compute_contrast(active_minus_rest,
output_type='effect_size')
###############################################################################
# In order to get statistical significance, we form a t-statistic, and
# directly convert it into z-scale. The z-scale means that the values
# are scaled to match a standard Gaussian distribution (mean=0,
# variance=1), across voxels, if there were no effects in the data.
#########################################################################
# So let's look at these computed contrasts:
#
# * 'left - right button press': probes motor activity in left versus right button presses
# * 'horizontal-vertical': probes the differential activity in viewing a horizontal vs vertical checkerboard
# * 'audio - visual': probes the difference of activity between listening to some content or reading the same type of content (instructions, stories)
# * 'computation - sentences': looks at the activity when performing a mental comptation task versus simply reading sentences.
#
contrasts = make_localizer_contrasts(design_matrix)
plt.figure(figsize=(5, 9))
from nilearn.reporting import plot_contrast_matrix
for i, (key, values) in enumerate(contrasts.items()):
ax = plt.subplot(len(contrasts) + 1, 1, i + 1)
plot_contrast_matrix(values, design_matrix=design_matrix, ax=ax)
plt.show()
#########################################################################
# Contrast estimation and plotting.
#
# Since this script will be repeated several times, for the sake of readability,
# we encapsulate it in a function that we call when needed.
#
from nilearn import plotting
def plot_contrast(first_level_model):
""" Given a first model, specify, estimate and plot the main contrasts"""
design_matrix = first_level_model.design_matrices_[0]
