mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i, :]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i, :]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(Image(aux.copy(), np.eye(4)))
return data_images, vardata_images, mask_images
data_images, vardata_images, mask_images = remake_images()
# zimg, mask = sm.onesample_test(data_images, None, mask_images, 'student')
zimg, mask, nulls = sm.onesample_test(
data_images, None, mask_images, "wilcoxon", permutations=1024, cluster_forming_th=0.01
)
clusters, info = sm.cluster_stats(zimg, mask, 0.01, nulls=nulls)
"""
# Save results
np.savez('group_result.npz', clusters=clusters, info=info)
"""
"""
# Load results
f = np.load('group_result.npz')
clusters = f['clusters']
info = f['info']
"""
def display_results_html(zmap_file_path, mask_file_path,
output_html_path, threshold=0.001,
method='fpr', cluster=0, null_zmax='bonferroni',
null_smax=None, null_s=None, nmaxima=4):
"""
Parameters
----------
zmap_file_path, string,
path of the input activation (z-transformed) image
mask_file_path, string,
path of the a corresponding mask image
output_html_path, string,
path where the output html should be written
threshold, float, optional
(p-variate) frequentist threshold of the activation image
method, string, optional
to be chosen as height_control in
nipy.neurospin.statistical_mapping
cluster, scalar, optional,
cluster size threshold
null_zmax: optional,
parameter for cluster leve statistics (?)
null_s: optional,
parameter for cluster leve statistics (?)
nmaxima: optional,
number of local maxima reported per supra-threshold cluster
"""
import nipy.neurospin.statistical_mapping as sm
# Read data: z-map and mask
zmap = load(zmap_file_path)
mask = load(mask_file_path)
cluster_th = cluster
# Compute cluster statistics
#if null_smax != None:
nulls={'zmax' : null_zmax, 'smax' : null_smax, 's' : null_s}
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
height_control=method.lower(),
cluster_th=cluster, nulls=nulls)
if clusters == None or info == None:
print "No results were written for %s" % zmap_file_path
return
# Make HTML page
output = open(output_html_path, mode = "w")
output.write("<html><head><title> Result Sheet for %s \
</title></head><body><center>\n" % zmap_file_path)
output.write("<h2> Results for %s</h2>\n" % zmap_file_path)
output.write("<table border = 1>\n")
output.write("<tr><th colspan=4> Voxel significance </th>\
<th colspan=3> Coordinates in MNI referential</th>\
<th>Cluster Size</th></tr>\n")
output.write("<tr><th>p FWE corr<br>(Bonferroni)</th>\
<th>p FDR corr</th><th>Z</th><th>p uncorr</th>")
output.write("<th> x (mm) </th><th> y (mm) </th><th> z (mm) </th>\
<th>(voxels)</th></tr>\n")
for cluster in clusters:
maxima = cluster['maxima']
size=cluster['size']
for j in range(min(len(maxima), nmaxima)):
temp = ["%f" % cluster['fwer_pvalue'][j]]
temp.append("%f" % cluster['fdr_pvalue'][j])
temp.append("%f" % cluster['zscore'][j])
temp.append("%f" % cluster['pvalue'][j])
for it in range(3):
temp.append("%f" % maxima[j][it])
if j == 0:
# Main local maximum
temp.append('%i'%size)
output.write('<tr><th align="center">' + '</th>\
<th align="center">'.join(temp) + '</th></tr>')
else:
# Secondary local maxima
output.write('<tr><td align="center">' + '</td>\
<td align="center">'.join(temp) + '</td><td></td></tr>\n')
nclust = len(clusters)
nvox = sum([clusters[k]['size'] for k in range(nclust)])
output.write("</table>\n")
output.write("Number of voxels : %i<br>\n" % nvox)
output.write("Number of clusters : %i<br>\n" % nclust)
output.write("Threshold Z = %f (%s control at %f)<br>\n" \
% (info['threshold_z'], method, threshold))
output.write("Cluster size threshold = %i voxels"%cluster_th)
output.write("</center></body></html>\n")
output.close()
def ComputeResultsContents(zmap_file_path, mask_file_path,
output_html_path, threshold=0.001,
method='fpr', cluster=0, null_zmax='bonferroni',
null_smax=None, null_s=None, nmaxima=4):
# Read data: z-map and mask
zmap = load(zmap_file_path)
mask = load(mask_file_path)
# Compute cluster statistics
#if null_smax != None:
nulls={'zmax' : null_zmax, 'smax' : null_smax, 's' : null_s}
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
height_control=method.lower(),
cluster_th=cluster, nulls=nulls)
if clusters == None or info == None:
print "No results were writen for %s" % zmap_file_path
return
#else:
# clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
# height_control=method, cluster_th=cluster,
# nulls={})
# Make HTML page
output = open(output_html_path, mode = "w")
output.write("<html><head><title> Result Sheet for %s \
</title></head><body><center>\n" % zmap_file_path)
output.write("<h2> Results for %s</h2>\n" % zmap_file_path)
output.write("<table border = 1>\n")
output.write("<tr><th colspan=4> Voxel significance </th>\
<th colspan=3> Coordinates in MNI referential</th>\
<th>Cluster Size</th></tr>\n")
output.write("<tr><th>p FWE corr<br>(Bonferroni)</th>\
<th>p FDR corr</th><th>Z</th><th>p uncorr</th>")
output.write("<th> x (mm) </th><th> y (mm) </th><th> z (mm) </th>\
<th>(voxels)</th></tr>\n")
for cluster in clusters:
maxima = cluster['maxima']
for j in range(min(len(maxima), nmaxima)):
temp = ["%f" % cluster['fwer_pvalue'][j]]
temp.append("%f" % cluster['fdr_pvalue'][j])
temp.append("%f" % cluster['zscore'][j])
temp.append("%f" % cluster['pvalue'][j])
for it in range(3):
temp.append("%f" % maxima[j][it])
if j == 0: ## Main local maximum
output.write('<tr><th align="center">' + '</th>\
<th align="center">'.join(temp) + '</th></tr>\n')
else: ## Secondary local maxima
output.write('<tr><td align="center">' + '</td>\
<td align="center">'.join(temp) + '</td><td></td></tr>\n')
output.write("</table>\n")
output.write("Number of voxels : %i<br>\n" % len(mask.get_data() > 0))
output.write("Threshold Z=%f (%s control at %f)<br>\n" \
% (info['threshold_z'], method, threshold))
output.write("</center></body></html>\n")
output.close()
def test_8():
img = make_surrogate_data()
clusters, info = cluster_stats(img, img, height_th=.001, height_control='fpr', cluster_th=0, nulls={})
nstv = sum([c['size'] for c in clusters])
assert nstv==36
def test_6():
img = make_surrogate_data()
clusters, info = cluster_stats(img, img, height_th=.05, height_control='fdr', cluster_th=0, nulls={})
print len(clusters), sum([c['size'] for c in clusters])
assert len(clusters)==4
def test_5():
img = make_surrogate_data()
clusters, info = cluster_stats(img, img, height_th=.05, height_control='bonferroni', cluster_th=0, nulls={})
assert len(clusters)==4
def test_4():
img = make_surrogate_data()
clusters, info = cluster_stats(img, img, height_th=.001, height_control='fpr', cluster_th=0, nulls={})
assert len(clusters)==4
def test3():
img = make_surrogate_data()
clusters, info = cluster_stats(img, img, height_th=3., height_control='None', cluster_th=10, nulls={})
assert len(clusters)==0