def test_get_clusters_table():
shape = (9, 10, 11)
data = np.zeros(shape)
data[2:4, 5:7, 6:8] = 5.
stat_img = nib.Nifti1Image(data, np.eye(4))
# test one cluster extracted
cluster_table = get_clusters_table(stat_img, 4, 0)
assert_true(len(cluster_table) == 1)
# test empty table on high stat threshold
cluster_table = get_clusters_table(stat_img, 6, 0)
assert_true(len(cluster_table) == 0)
# test empty table on high cluster threshold
cluster_table = get_clusters_table(stat_img, 4, 9)
assert_true(len(cluster_table) == 0)
def test_get_clusters_table():
shape = (9, 10, 11)
data = np.zeros(shape)
data[2:4, 5:7, 6:8] = 5.
stat_img = nib.Nifti1Image(data, np.eye(4))
# test one cluster extracted
cluster_table = get_clusters_table(stat_img, 4, 0)
assert_true(len(cluster_table) == 1)
# test empty table on high stat threshold
cluster_table = get_clusters_table(stat_img, 6, 0)
assert_true(len(cluster_table) == 0)
# test empty table on high cluster threshold
cluster_table = get_clusters_table(stat_img, 4, 9)
assert_true(len(cluster_table) == 0)
def _make_stat_maps_contrast_clusters(stat_img, contrasts_plots, threshold,
alpha, cluster_threshold, height_control,
min_distance, bg_img, display_mode,
plot_type):
""" Populates a smaller HTML sub-template with the proper values,
make a list containing one or more of such components
& returns the list to be inserted into the HTML Report Template.
Each component contains the HTML code for
a contrast & its corresponding statistical maps & cluster table;
Parameters
----------
stat_img : Niimg-like object or None
statistical image (presumably in z scale)
whenever height_control is 'fpr' or None,
stat_img=None is acceptable.
If it is 'fdr' or 'bonferroni',
an error is raised if stat_img is None.
contrasts_plots: Dict[str, str]
Contains the contrast names & the HTML code of the contrast's SVG plot.
threshold: float
desired threshold in z-scale.
This is used only if height_control is None
alpha: float
number controlling the thresholding (either a p-value or q-value).
Its actual meaning depends on the height_control parameter.
This function translates alpha to a z-scale threshold.
cluster_threshold : float
cluster size threshold. In the returned thresholded map,
sets of connected voxels (`clusters`) with size smaller
than this number will be removed.
height_control: string
false positive control meaning of cluster forming
threshold: 'fpr' or 'fdr' or 'bonferroni' or None
min_distance: `float`
For display purposes only.
Minimum distance between subpeaks in mm. Default is 8 mm.
bg_img : Niimg-like object
Only used when plot_type is 'slice'.
See http://nilearn.github.io/manipulating_images/input_output.html
The background image for stat maps to be plotted on upon.
If nothing is specified, the MNI152 template will be used.
To turn off background image, just pass "bg_img=False".
display_mode: string
Choose the direction of the cuts:
'x' - sagittal, 'y' - coronal, 'z' - axial,
'l' - sagittal left hemisphere only,
'r' - sagittal right hemisphere only,
'ortho' - three cuts are performed in orthogonal directions.
Possible values are:
'ortho', 'x', 'y', 'z', 'xz', 'yx', 'yz',
'l', 'r', 'lr', 'lzr', 'lyr', 'lzry', 'lyrz'.
plot_type: string
['slice', 'glass']
The type of plot to be drawn.
Returns
-------
all_components: List[String]
Each element is a set of HTML code for
contrast name, contrast plot, statistical map, cluster table.
"""
all_components = []
components_template_path = os.path.join(
HTML_TEMPLATE_ROOT_PATH, 'stat_maps_contrast_clusters_template.html')
with open(components_template_path) as html_template_obj:
components_template_text = html_template_obj.read()
for contrast_name, stat_map_img in stat_img.items():
component_text_ = string.Template(components_template_text)
thresholded_stat_map, threshold = map_threshold(
stat_img=stat_map_img,
threshold=threshold,
alpha=alpha,
cluster_threshold=cluster_threshold,
height_control=height_control,
)
table_details = _clustering_params_to_dataframe(
threshold,
cluster_threshold,
min_distance,
height_control,
alpha,
)
stat_map_svg = _stat_map_to_svg(
stat_img=thresholded_stat_map,
bg_img=bg_img,
display_mode=display_mode,
plot_type=plot_type,
table_details=table_details,
)
cluster_table = get_clusters_table(
stat_map_img,
stat_threshold=threshold,
cluster_threshold=cluster_threshold,
min_distance=min_distance,
)
cluster_table_html = _dataframe_to_html(
cluster_table,
precision=2,
index=False,
classes='cluster-table',
)
table_details_html = _dataframe_to_html(
table_details,
precision=2,
header=False,
classes='cluster-details-table',
)
components_values = {
'contrast_name': escape(contrast_name),
'contrast_plot': contrasts_plots[contrast_name],
'stat_map_img': stat_map_svg,
'cluster_table_details': table_details_html,
'cluster_table': cluster_table_html,
}
component_text_ = component_text_.safe_substitute(**components_values)
all_components.append(component_text_)
return all_components
display_mode='z', cut_coords=3, black_bg=True,
title='Active minus Rest (fdr=0.05), clusters > 10 voxels')
plt.show()
###############################################################################
# We can save the effect and zscore maps to the disk
z_map.to_filename(join(outdir, 'active_vs_rest_z_map.nii.gz'))
eff_map.to_filename(join(outdir, 'active_vs_rest_eff_map.nii.gz'))
###############################################################################
# Report the found positions in a table
from nistats.reporting import get_clusters_table
table = get_clusters_table(z_map, stat_threshold=threshold,
cluster_threshold=20)
print(table)
###############################################################################
# the table can be saved for future use
table.to_csv(join(outdir, 'table.csv'))
###############################################################################
# Performing an F-test
#
# "active vs rest" is a typical t test: condition versus
# baseline. Another popular type of test is an F test in which one
# seeks whether a certain combination of conditions (possibly two-,
# three- or higher-dimensional) explains a significant proportion of
# the signal. Here one might for instance test which voxels are well
cut_coords=3,
black_bg=True,
title='Active minus Rest (fdr=0.05), clusters > 10 voxels')
plt.show()
###############################################################################
# We can save the effect and zscore maps to the disk
z_map.to_filename(join(outdir, 'active_vs_rest_z_map.nii.gz'))
eff_map.to_filename(join(outdir, 'active_vs_rest_eff_map.nii.gz'))
###############################################################################
# Report the found positions in a table
from nistats.reporting import get_clusters_table
table = get_clusters_table(z_map,
stat_threshold=threshold,
cluster_threshold=20)
print(table)
###############################################################################
# the table can be saved for future use
table.to_csv(join(outdir, 'table.csv'))
###############################################################################
# Performing an F-test
#
# "active vs rest" is a typical t test: condition versus
# baseline. Another popular type of test is an F test in which one
# seeks whether a certain combination of conditions (possibly two-,
# three- or higher-dimensional) explains a significant proportion of
group_dir = os.path.join(data_dir, task, 'derivatives', 'pypreprocess',
'group')
if not os.path.exists(group_dir):
os.mkdir(group_dir)
from nistats.second_level_model import SecondLevelModel
for contrast_id in contrasts.keys():
cmap_filenames = [
os.path.join(data_dir, task, 'derivatives', 'spmpreproc_%s' % session,
subject, 'glm', '%s_effects.nii.gz' % contrast_id)
for subject in subjects
]
second_level_model = SecondLevelModel(smoothing_fwhm=5).fit(
cmap_filenames, design_matrix=group_design_matrix)
z_map = second_level_model.compute_contrast(output_type='z_score')
thresholded_map, threshold = map_threshold(z_map,
level=.1,
height_control='fdr',
cluster_threshold=10)
z_map.to_filename(os.path.join(group_dir, '%s_z_map.nii.gz' % contrast_id))
output_file = os.path.join(group_dir, '%s_z_map.png' % contrast_id)
plotting.plot_stat_map(thresholded_map,
title='%s, fdr = .1' % contrast_id,
threshold=threshold,
output_file=output_file)
clusters_table = get_clusters_table(z_map, 3., 10)
clusters_table.to_csv(
os.path.join(group_dir, '%s_clusters.csv' % contrast_id))
# plotting.show()
# -----------------------------------------------------
# We display the contrast plot and table with cluster information.
from nistats.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 nistats.reporting import get_clusters_table
print(get_clusters_table(z_map, norm.isf(0.001), 10).to_latex())
#########################################################################
# Generating a report
# -------------------
# Using the computed FirstLevelModel and contrast information,
# we can quickly create a summary report.
from nistats.reporting import make_glm_report
report = make_glm_report(
model=model,
contrasts='StopSuccess - Go',
)
#########################################################################
# Calculate and plot contrast
# ---------------------------
from nilearn import plotting
z_map = fmri_glm.compute_contrast('active - rest')
plotting.plot_stat_map(z_map, bg_img=mean_img, threshold=3.1)
#########################################################################
# Extract the largest clusters
# ----------------------------
from nistats.reporting import get_clusters_table
from nilearn import input_data
table = get_clusters_table(z_map, stat_threshold=3.1,
cluster_threshold=20).set_index('Cluster ID',
drop=True)
table.head()
# get the 6 largest clusters' max x, y, and z coordinates
coords = table.loc[range(1, 7), ['X', 'Y', 'Z']].values
# extract time series from each coordinate
masker = input_data.NiftiSpheresMasker(coords)
real_timeseries = masker.fit_transform(fmri_img)
predicted_timeseries = masker.fit_transform(fmri_glm.predicted[0])
#########################################################################
# Plot predicted and actual time series for 6 most significant clusters
# ---------------------------------------------------------------------
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from scipy.stats import norm
plotting.plot_glass_brain(z_map, colorbar=True, threshold=norm.isf(0.001),
title='Nistats Z map of "StopSuccess - Go" (unc p<0.001)',
plot_abs=False, display_mode='ortho')
plotting.plot_glass_brain(fsl_z_map, colorbar=True, threshold=norm.isf(0.001),
title='FSL Z map of "StopSuccess - Go" (unc p<0.001)',
plot_abs=False, display_mode='ortho')
plt.show()
from nistats.reporting import compare_niimgs
compare_niimgs([z_map], [fsl_z_map], model.masker_,
ref_label='Nistats', src_label='FSL')
plt.show()
#############################################################################
# Simple statistical report of thresholded contrast
# -----------------------------------------------------
# We display the contrast plot and table with cluster information
from nistats.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
from nistats.reporting import get_clusters_table
print(get_clusters_table(z_map, norm.isf(0.001), 10).to_latex())