def plot(self, limit=5, anatomical=None):
""" Create a quick plot of self.data. Will plot each image separately
Args:
limit: max number of images to return
anatomical: nifti image or file name to overlay
"""
if anatomical is not None:
if not isinstance(anatomical, nib.Nifti1Image):
if type(anatomical) is str:
anatomical = nib.load(anatomical)
else:
raise ValueError("anatomical is not a nibabel instance")
else:
anatomical = get_anatomical()
if self.data.ndim == 1:
plot_stat_map(self.to_nifti(), anatomical, cut_coords=range(-40, 50, 10), display_mode='z',
black_bg=True, colorbar=True, draw_cross=False)
else:
for i in xrange(self.data.shape[0]):
if i < limit:
# plot_roi(self.nifti_masker.inverse_transform(self.data[i,:]), self.anatomical)
# plot_stat_map(self.nifti_masker.inverse_transform(self.data[i,:]),
plot_stat_map(self[i].to_nifti(), anatomical, cut_coords=range(-40, 50, 10), display_mode='z',
black_bg=True, colorbar=True, draw_cross=False)
def test_singleton_ax_dim():
for axis, direction in enumerate("xyz"):
shape = [5, 6, 7]
shape[axis] = 1
img = nibabel.Nifti1Image(np.ones(shape), np.eye(4))
plot_stat_map(img, None, display_mode=direction)
plt.close()
def test_plotting_functions_with_cmaps():
img = load_mni152_template()
cmaps = ['Paired', 'Set1', 'Set2', 'Set3']
for cmap in cmaps:
plot_roi(img, cmap=cmap, colorbar=True)
plot_stat_map(img, cmap=cmap, colorbar=True)
plot_glass_brain(img, cmap=cmap, colorbar=True)
if LooseVersion(matplotlib.__version__) >= LooseVersion('2.0.0'):
plot_stat_map(img, cmap='viridis', colorbar=True)
plt.close()
def test_plot_stat_map_with_nans():
img = _generate_img()
data = img.get_data()
data[6, 5, 1] = np.nan
data[1, 5, 2] = np.nan
data[1, 3, 2] = np.nan
data[6, 5, 2] = np.inf
img = nibabel.Nifti1Image(data, mni_affine)
plot_epi(img)
plot_stat_map(img)
plot_glass_brain(img)
def test_plot_functions():
img = _generate_img()
# smoke-test for each plotting function with default arguments
for plot_func in [plot_anat, plot_img, plot_stat_map, plot_epi, plot_glass_brain]:
with tempfile.NamedTemporaryFile(suffix=".png") as fp:
plot_func(img, output_file=fp.name)
# test for bad input arguments (cf. #510)
ax = plt.subplot(111, rasterized=True)
with tempfile.NamedTemporaryFile(suffix=".png") as fp:
plot_stat_map(img, symmetric_cbar=True, output_file=fp.name, axes=ax, vmax=np.nan)
plt.close()
def test_save_plot():
img = _generate_img()
kwargs_list = [{}, {'display_mode': 'x', 'cut_coords': 3}]
for kwargs in kwargs_list:
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
display = plot_stat_map(img, output_file=fp.name, **kwargs)
assert_true(display is None)
display = plot_stat_map(img, **kwargs)
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
display.savefig(fp.name)
def test_plotting_functions_with_cmaps():
img = load_mni152_template()
# some cmaps such as 'viridis' (the new default in 2.0), 'magma', 'plasma',
# and 'inferno' are not supported for older matplotlib version from < 1.5
cmaps = ['Paired', 'Set1', 'Set2', 'Set3']
for cmap in cmaps:
plot_roi(img, cmap=cmap, colorbar=True)
plot_stat_map(img, cmap=cmap, colorbar=True)
plot_glass_brain(img, cmap=cmap, colorbar=True)
if LooseVersion(matplotlib.__version__) >= LooseVersion('2.0.0'):
plot_stat_map(img, cmap='viridis', colorbar=True)
plt.close()
def test_save_plot():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
kwargs_list = [{}, {'display_mode': 'x', 'cut_coords': 3}]
for kwargs in kwargs_list:
with tempfile.TemporaryFile(suffix='.png') as fp:
display = plot_stat_map(img, output_file=fp.name, **kwargs)
assert_true(display is None)
display = plot_stat_map(img, **kwargs)
with tempfile.TemporaryFile(suffix='.png') as fp:
display.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_save_plot():
img = _generate_img()
kwargs_list = [{}, {'display_mode': 'x', 'cut_coords': 3}]
for kwargs in kwargs_list:
filename = tempfile.mktemp(suffix='.png')
try:
display = plot_stat_map(img, output_file=filename, **kwargs)
finally:
os.remove(filename)
assert_true(display is None)
display = plot_stat_map(img, **kwargs)
filename = tempfile.mktemp(suffix='.png')
try:
display.savefig(filename)
finally:
os.remove(filename)
def test_plot_functions():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
# smoke-test for each plotting function with default arguments
for plot_func in [plot_anat, plot_img, plot_stat_map, plot_epi,
plot_glass_brain]:
with tempfile.TemporaryFile(suffix='.png') as fp:
plot_func(img, output_file=fp.name)
# test for bad input arguments (cf. #510)
ax = plt.subplot(111, rasterized=True)
with tempfile.TemporaryFile(suffix='.png') as fp:
plot_stat_map(
img, symmetric_cbar=True,
output_file=fp.name,
axes=ax, vmax=np.nan)
plt.close()
def test_plot_functions():
img = _generate_img()
# smoke-test for each plotting function with default arguments
for plot_func in [plot_anat, plot_img, plot_stat_map, plot_epi,
plot_glass_brain]:
filename = tempfile.mktemp(suffix='.png')
try:
plot_func(img, output_file=filename)
finally:
os.remove(filename)
# test for bad input arguments (cf. #510)
ax = plt.subplot(111, rasterized=True)
filename = tempfile.mktemp(suffix='.png')
try:
plot_stat_map(img, symmetric_cbar=True,
output_file=filename,
axes=ax, vmax=np.nan)
finally:
os.remove(filename)
plt.close()
def test_plot_stat_map_threshold_for_affine_with_rotation():
# threshold was not being applied when affine has a rotation
# see https://github.com/nilearn/nilearn/issues/599 for more details
data = np.random.randn(10, 10, 10)
# matrix with rotation
affine = np.array([[-3.0, 1.0, 0.0, 1.0], [-1.0, -3.0, 0.0, -2.0], [0.0, 0.0, 3.0, 3.0], [0.0, 0.0, 0.0, 1.0]])
img = nibabel.Nifti1Image(data, affine)
display = plot_stat_map(img, bg_img=None, threshold=1e6, display_mode="z", cut_coords=1)
# Next two lines retrieve the numpy array from the plot
ax = list(display.axes.values())[0].ax
plotted_array = ax.images[0].get_array()
# Given the high threshold the array should be entirely masked
assert_true(plotted_array.mask.all())
def test_plot_stat_map_threshold_for_uint8():
# mask was applied in [-threshold, threshold] which is problematic
# for uint8 data. See https://github.com/nilearn/nilearn/issues/611
# for more details
data = 10 * np.ones((10, 10, 10), dtype='uint8')
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine)
threshold = np.array(5, dtype='uint8')
display = plot_stat_map(img, bg_img=None, threshold=threshold,
display_mode='z', cut_coords=1)
# Next two lines retrieve the numpy array from the plot
ax = list(display.axes.values())[0].ax
plotted_array = ax.images[0].get_array()
# Make sure that no data is masked
assert_equal(plotted_array.mask.sum(), 0)
def test_plot_stat_map_threshold_for_uint8():
# mask was applied in [-threshold, threshold] which is problematic
# for uint8 data. See https://github.com/nilearn/nilearn/issues/611
# for more details
data = 10 * np.ones((10, 10, 10), dtype="uint8")
# Having a zero minimum value is important to reproduce
# https://github.com/nilearn/nilearn/issues/762
data[0, 0, 0] = 0
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine)
threshold = np.array(5, dtype="uint8")
display = plot_stat_map(img, bg_img=None, threshold=threshold, display_mode="z", cut_coords=[0])
# Next two lines retrieve the numpy array from the plot
ax = list(display.axes.values())[0].ax
plotted_array = ax.images[0].get_array()
# Make sure that there is one value masked
assert_equal(plotted_array.mask.sum(), 1)
# Make sure that the value masked is in the corner. Note that the
# axis orientation seem to be flipped, hence (0, 0) -> (-1, 0)
assert_true(plotted_array.mask[-1, 0])
def test_plot_stat_map_threshold_for_affine_with_rotation():
# threshold was not being applied when affine has a rotation
# see https://github.com/nilearn/nilearn/issues/599 for more details
data = np.random.randn(10, 10, 10)
# matrix with rotation
affine = np.array([[-3., 1., 0., 1.], [-1., -3., 0., -2.],
[0., 0., 3., 3.], [0., 0., 0., 1.]])
img = nibabel.Nifti1Image(data, affine)
display = plot_stat_map(img,
bg_img=None,
threshold=1e6,
display_mode='z',
cut_coords=1)
# Next two lines retrieve the numpy array from the plot
ax = list(display.axes.values())[0].ax
plotted_array = ax.images[0].get_array()
# Given the high threshold the array should be entirely masked
assert_true(plotted_array.mask.all())
# Save execution time and memory
plt.close()
def test_outlier_cut_coords():
""" Test to plot a subset of a large set of cuts found for a small area."""
bg_img = load_mni152_template()
data = np.zeros((79, 95, 79))
affine = np.array([[-2., 0., 0., 78.], [0., 2., 0., -112.],
[0., 0., 2., -70.], [0., 0., 0., 1.]])
# Color a cube around a corner area:
x, y, z = 20, 22, 60
x_map, y_map, z_map = coord_transform(x, y, z, np.linalg.inv(affine))
data[int(x_map) - 1:int(x_map) + 1,
int(y_map) - 1:int(y_map) + 1,
int(z_map) - 1:int(z_map) + 1] = 1
img = nibabel.Nifti1Image(data, affine)
cuts = find_cut_slices(img, n_cuts=20, direction='z')
p = plot_stat_map(img,
display_mode='z',
cut_coords=cuts[-4:],
bg_img=bg_img)
def test_outlier_cut_coords():
""" Test to plot a subset of a large set of cuts found for a small area."""
bg_img = load_mni152_template()
data = np.zeros((79, 95, 79))
affine = np.array([[ -2., 0., 0., 78.],
[ 0., 2., 0., -112.],
[ 0., 0., 2., -70.],
[ 0., 0., 0., 1.]])
# Color a cube around a corner area:
x, y, z = 20, 22, 60
x_map, y_map, z_map = coord_transform(x, y, z,
np.linalg.inv(affine))
data[int(x_map) - 1:int(x_map) + 1,
int(y_map) - 1:int(y_map) + 1,
int(z_map) - 1:int(z_map) + 1] = 1
img = nibabel.Nifti1Image(data, affine)
cuts = find_cut_slices(img, n_cuts=20, direction='z')
p = plot_stat_map(img, display_mode='z', cut_coords=cuts[-4:],
bg_img=bg_img)
def test_plot_stat_map_threshold_for_uint8():
# mask was applied in [-threshold, threshold] which is problematic
# for uint8 data. See https://github.com/nilearn/nilearn/issues/611
# for more details
data = 10 * np.ones((10, 10, 10), dtype='uint8')
# Having a zero minimum value is important to reproduce
# https://github.com/nilearn/nilearn/issues/762
data[0, 0, 0] = 0
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine)
threshold = np.array(5, dtype='uint8')
display = plot_stat_map(img, bg_img=None, threshold=threshold,
display_mode='z', cut_coords=[0])
# Next two lines retrieve the numpy array from the plot
ax = list(display.axes.values())[0].ax
plotted_array = ax.images[0].get_array()
# Make sure that there is one value masked
assert_equal(plotted_array.mask.sum(), 1)
# Make sure that the value masked is in the corner. Note that the
# axis orientation seem to be flipped, hence (0, 0) -> (-1, 0)
assert_true(plotted_array.mask[-1, 0])
# Save execution time and memory
plt.close()
def test_plot_stat_map():
img = _generate_img()
plot_stat_map(img, cut_coords=(80, -120, -60))
# Smoke test coordinate finder, with and without mask
masked_img = nibabel.Nifti1Image(
np.ma.masked_equal(img.get_data(), 0),
mni_affine)
plot_stat_map(masked_img, display_mode='x')
plot_stat_map(img, display_mode='y', cut_coords=2)
# 'yx' display_mode
plot_stat_map(img, display_mode='yx')
# regression test #510
data = np.zeros((91, 109, 91))
aff = np.eye(4)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
rng = np.random.RandomState(42)
data = rng.randn(91, 109, 91)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
# Save execution time and memory
plt.close()
def test_plot_stat_map():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
plot_stat_map(img, cut_coords=(80, -120, -60))
# Smoke test coordinate finder, with and without mask
masked_img = nibabel.Nifti1Image(np.ma.masked_equal(img.get_data(), 0),
mni_affine)
plot_stat_map(masked_img, display_mode='x')
plot_stat_map(img, display_mode='y', cut_coords=2)
# 'yx' display_mode
plot_stat_map(img, display_mode='yx')
# regression test #510
data = np.zeros((91, 109, 91))
aff = np.eye(4)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
rng = np.random.RandomState(42)
data = rng.randn(91, 109, 91)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
def test_plot_stat_map():
mp.use('template', warn=False)
import matplotlib.pyplot as plt
plt.switch_backend('template')
img = _generate_img()
plot_stat_map(img, cut_coords=(80, -120, -60))
# Smoke test coordinate finder, with and without mask
masked_img = nibabel.Nifti1Image(
np.ma.masked_equal(img.get_data(), 0),
mni_affine)
plot_stat_map(masked_img, display_mode='x')
plot_stat_map(img, display_mode='y', cut_coords=2)
# 'yx' display_mode
plot_stat_map(img, display_mode='yx')
# regression test #510
data = np.zeros((91, 109, 91))
aff = np.eye(4)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
rng = np.random.RandomState(42)
data = rng.randn(91, 109, 91)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
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()
def test_singleton_ax_dim():
for axis, direction in enumerate("xyz"):
shape = [5, 6, 7]
shape[axis] = 1
img = nibabel.Nifti1Image(np.ones(shape), np.eye(4))
plot_stat_map(img, None, display_mode=direction)
data = resampled_nii.get_data()
data[data != 0] = 1
if len(data.shape) == 4:
data.shape = data.shape[:3]
freq_map_data += data
return nb.Nifti1Image(freq_map_data, target_nii.get_affine())
if __name__ == '__main__':
combined_df = get_images_with_collections_df()
print combined_df[['DOI', 'url_collection', 'name_image', 'file']]
#restrict to Z-, F-, or T-maps
combined_df = combined_df[combined_df['map_type'].isin(["Z","F","T"])]
print combined_df["name_collection"].value_counts()
dest_dir = "/tmp/neurovault_analysis"
target = "/usr/share/fsl/data/standard/MNI152_T1_2mm.nii.gz"
download_and_resample(combined_df, dest_dir, target)
freq_nii = get_frequency_map(combined_df, dest_dir, target)
plot_stat_map(freq_nii, "/usr/share/fsl/data/standard/MNI152_T1_2mm.nii.gz",
display_mode="z")
freq_nii.to_filename("/tmp/freq_map.nii.gz")
plt.show()
combined_df.to_csv('%s/metadata.csv' % dest_dir, encoding='utf8')
def test_plot_stat_map():
img = _generate_img()
plot_stat_map(img, cut_coords=(80, -120, -60))
# Smoke test coordinate finder, with and without mask
masked_img = nibabel.Nifti1Image(
np.ma.masked_equal(img.get_data(), 0),
mni_affine)
plot_stat_map(masked_img, display_mode='x')
plot_stat_map(img, display_mode='y', cut_coords=2)
# 'yx' display_mode
plot_stat_map(img, display_mode='yx')
# regression test #510
data = np.zeros((91, 109, 91))
aff = np.eye(4)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
rng = np.random.RandomState(42)
data = rng.randn(91, 109, 91)
new_img = nibabel.Nifti1Image(data, aff)
plot_stat_map(new_img, threshold=1000, colorbar=True)
# Save execution time and memory
plt.close()
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()