def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
z_slicer = plot_anat(display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
ortho_slicer = plot_anat(img, dim='auto')
filename = tempfile.mktemp(suffix='.png')
try:
ortho_slicer.savefig(filename)
finally:
os.remove(filename)
# Save execution time and memory
plt.close()
def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
z_slicer = plot_anat(display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
ortho_slicer = plot_anat(img, dim='auto')
filename = tempfile.mktemp(suffix='.png')
try:
ortho_slicer.savefig(filename)
finally:
os.remove(filename)
# Save execution time and memory
plt.close()
def test_plot_functions():
# This is only a smoke test
mp.use('template', warn=False)
import pylab as pl
pl.switch_backend('template')
data_positive = np.zeros((7, 7, 3))
data_positive[1:-1, 2:-1, 1:] = 1
data_negative = -data_positive
rng = np.random.RandomState(42)
data_heterogeneous = data_positive * rng.randn(*data_positive.shape)
img_positive = nibabel.Nifti1Image(data_positive, mni_affine)
img_negative = nibabel.Nifti1Image(data_negative, mni_affine)
img_heterogeneous = nibabel.Nifti1Image(data_heterogeneous, mni_affine)
# Test saving with empty plot
ax = pl.subplot(111, rasterized=True)
z_slicer = plot_anat(anat_img=False, display_mode='z', axes=ax)
z_slicer = plot_anat(display_mode='z', axes=ax)
z_slicer.add_edges(img_positive, color='c')
z_slicer.savefig(tempfile.TemporaryFile())
pl.close()
for img in [img_positive, img_negative, img_heterogeneous]:
ortho_slicer = plot_anat(img, dim=True)
ortho_slicer.savefig(tempfile.TemporaryFile())
pl.close()
for func in [
plot_anat, plot_img, plot_stat_map, plot_epi, plot_glass_brain,
partial(plot_stat_map, symmetric_cbar=True),
partial(plot_stat_map, symmetric_cbar=False),
partial(plot_stat_map, symmetric_cbar=False, vmax=10),
partial(plot_stat_map, symmetric_cbar=True, vmax=10),
partial(plot_stat_map, colorbar=False)
]:
ax = pl.subplot(111, rasterized=True)
ortho_slicer = func(img, cut_coords=(80, -120, -60), axes=ax)
# Saving forces a draw, and thus smoke-tests the axes locators
ortho_slicer.savefig(tempfile.TemporaryFile())
ortho_slicer.add_edges(img, color='c')
pl.close()
# Smoke test coordinate finder, with and without mask
masked_img = nibabel.Nifti1Image(
np.ma.masked_equal(img.get_data(), 0), mni_affine)
ax = pl.subplot(111, rasterized=True)
func(masked_img, display_mode='x', axes=ax)
pl.close()
ax = pl.subplot(111, rasterized=True)
func(img, display_mode='y', axes=ax)
pl.close()
ax = pl.subplot(111, rasterized=True)
out = func(img,
output_file=tempfile.TemporaryFile(suffix='.png'),
axes=ax)
assert_true(out is None)
pl.close()
def test_plot_img_empty():
# Test that things don't crash when we give a map with nothing above
# threshold
# This is only a smoke test
mp.use('template', warn=False)
import pylab as pl
pl.switch_backend('template')
data = np.zeros((20, 20, 20))
img = nibabel.Nifti1Image(data, mni_affine)
plot_anat(img)
slicer = plot_img(img, display_mode='y', threshold=1)
slicer.close()
pl.close('all')
def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
z_slicer = plot_anat(display_mode='z')
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
ortho_slicer = plot_anat(img, dim=True)
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
ortho_slicer.savefig(fp.name)
def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
z_slicer = plot_anat(display_mode='z')
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
ortho_slicer = plot_anat(img, dim=True)
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
ortho_slicer.savefig(fp.name)
def test_plot_anat(testdata_3d, tmpdir):
img = testdata_3d['img']
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
filename = str(tmpdir.join('test.png'))
z_slicer.savefig(filename)
z_slicer = plot_anat(display_mode='z')
z_slicer.savefig(filename)
ortho_slicer = plot_anat(img, dim='auto')
ortho_slicer.savefig(filename)
# Save execution time and memory
plt.close()
def test_plot_anat():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
with tempfile.TemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
z_slicer = plot_anat(display_mode='z')
with tempfile.TemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
ortho_slicer = plot_anat(img, dim=True)
with tempfile.TemporaryFile(suffix='.png') as fp:
ortho_slicer.savefig(fp.name)
def test_plot_anat():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
with tempfile.TemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
z_slicer = plot_anat(display_mode='z')
with tempfile.TemporaryFile(suffix='.png') as fp:
z_slicer.savefig(fp.name)
ortho_slicer = plot_anat(img, dim=True)
with tempfile.TemporaryFile(suffix='.png') as fp:
ortho_slicer.savefig(fp.name)
def test_plotting_functions_with_nans_in_bg_img():
bg_img = _generate_img()
bg_data = bg_img.get_data()
bg_data[6, 5, 1] = np.nan
bg_data[1, 5, 2] = np.nan
bg_data[1, 3, 2] = np.nan
bg_data[6, 5, 2] = np.inf
bg_img = nibabel.Nifti1Image(bg_data, mni_affine)
plot_anat(bg_img)
# test with plot_roi passing background image which contains nans values
# in it
roi_img = _generate_img()
plot_roi(roi_img=roi_img, bg_img=bg_img)
stat_map_img = _generate_img()
plot_stat_map(stat_map_img=stat_map_img, bg_img=bg_img)
plt.close()
def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
z_slicer = plot_anat(display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
ortho_slicer = plot_anat(img, dim=True)
filename = tempfile.mktemp(suffix='.png')
try:
ortho_slicer.savefig(filename)
finally:
os.remove(filename)
def test_plot_anat():
img = _generate_img()
# Test saving with empty plot
z_slicer = plot_anat(anat_img=False, display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
z_slicer = plot_anat(display_mode='z')
filename = tempfile.mktemp(suffix='.png')
try:
z_slicer.savefig(filename)
finally:
os.remove(filename)
ortho_slicer = plot_anat(img, dim=True)
filename = tempfile.mktemp(suffix='.png')
try:
ortho_slicer.savefig(filename)
finally:
os.remove(filename)
def test_plotting_functions_with_display_mode_tiled():
img = _generate_img()
plot_stat_map(img, display_mode='tiled')
plot_anat(display_mode='tiled')
plot_img(img, display_mode='tiled')
plt.close()
combined_df.to_csv('%s/metadata.tsv' % dest_dir, encoding='utf8',sep="\t")
# Example 1: Cognitive Decoding with NeuroSynth
terms_df = get_neurosynth_terms(combined_df)
print combined_df["name_collection"].value_counts()
combined_df = combined_df.merge(terms_df, on=['image_id', ])
# 3) Do analysis
#--------------------------------------------------
# Plot a map of frequency of activation
freq_nii = get_frequency_map(combined_df, dest_dir, target)
freq_nii.to_filename("%s/freq_map.nii.gz" %dest_dir)
display = plot_anat("/usr/share/fsl/data/standard/MNI152_T1_2mm.nii.gz",
display_mode='z',
cut_coords=np.linspace(-30, 60, 7))
display.add_overlay(freq_nii, vmin=0, cmap=plt.cm.hot,colorbar=True)
display._colorbar_ax.set_yticklabels(["% 3i" % float(t.get_text()) for t in display._colorbar_ax.yaxis.get_ticklabels()])
display.title('Percentage of activations (Z or T > 3)')
display.savefig('%s/activation_frequency.png' %(dest_dir))
display.savefig('%s/activation_frequency.pdf' %(dest_dir))
#--------------------------------------------------
# Plot the frequency of occurence of neurosynth terms
# Use the terms from neurosynth to label the ICAs
terms = combined_df[[c for c in combined_df.columns if c.startswith('neurosynth decoding')]]
terms = terms.fillna(0)
term_matrix = terms.as_matrix()
# Labels that have a negative correlation are not present in the map
def test_plotting_functions_with_display_mode_tiled(testdata_3d):
img = testdata_3d['img']
plot_stat_map(img, display_mode='tiled')
plot_anat(display_mode='tiled')
plot_img(img, display_mode='tiled')
plt.close()
def plot_mr(input_mr,output_png):
display = plot_anat(standard,display_mode='z',cut_coords=np.linspace(-30, 60, 7))
display.add_overlay(newimage, vmin=0,cmap=plt.cm.hot, colorbar=True)
display._colorbar_ax.set_yticklabels(["% 3i" % (100 * float(t.get_text())) for t in display._colorbar_ax.yaxis.get_ticklabels()])
display.title("Vanessa's First Plot in Nilearn!'")
display.savefig(output_png)
