from nisl import datasets, utils
from pynax import Figure, Mark, create_axes
import pylab as pl
import numpy as np
nyu = datasets.fetch_nyu_rest(n_subjects=1)
func = nyu.func[0]
niimg = utils.check_niimg(func)
fig = Figure((2, 3))
data = niimg.get_data()[..., 0]
# Awesome example activation map : take whatever is > .6 max
data_act = np.ma.MaskedArray(data, mask=(data < .6 * np.max(data)))
display_options = {}
display_options['interpolation'] = 'nearest'
display_options['cmap'] = pl.cm.gray
x, y, z = create_axes(['x', 'y', 'z'])
mx = Mark(x, 20)
my = Mark(y, 20)
mz = Mark(z, 20)
vx = fig.add((1, 2), data, mx, z, y, display_options=display_options)
vx.add_mark(my)
vx.add_mark(mz)
vy = fig.add((0, 2), data, my, z, x, display_options=display_options)
vy.add_mark(mx)
vy.add_mark(mz)
vz = fig.add((0, 0),
data,
mz,
y,
if len(sys.argv) > 1:
import nibabel as nb
f = nb.load(sys.argv[1])
data = f.get_data()
data_act = None
else:
nyu = datasets.fetch_nyu_rest(n_subjects=1)
func = nyu.func[0]
niimg = utils.check_niimg(func)
data = niimg.get_data()[..., 0]
# Awesome example activation map : take whatever is > .6 max
data_act = np.ma.MaskedArray(data, mask=(data < .6 * np.max(data)))
fig = Figure((2, 3))
display_options = {}
display_options['interpolation'] = 'nearest'
display_options['cmap'] = pl.cm.gray
display_options['pynax_colorbar'] = True
x, y, z = create_axes(['x', 'y', 'z'])
mx = Mark(x, 20)
my = Mark(y, 20)
mz = Mark(z, 20)
vx = fig.add((1, 2), data, mx, z, y, display_options=display_options)
vx.add_mark(my)
vx.add_mark(mz)
vy = fig.add((0, 2), data, my, z, x, display_options=display_options)
vy.add_mark(mx)
vy.add_mark(mz)
from nisl import datasets, utils
from pynax import Figure, Mark, create_axes
import pylab as pl
import numpy as np
nyu = datasets.fetch_nyu_rest(n_subjects=1)
func = nyu.func[0]
niimg = utils.check_niimg(func)
fig = Figure((2, 7))
data = niimg.get_data()[..., 0]
# Awesome example activation map : take whatever is > .6 max
data_act = np.ma.MaskedArray(data, mask=(data < .6 * np.max(data)))
display_options = {}
display_options['interpolation'] = 'nearest'
display_options['cmap'] = pl.cm.gray
x, y, z = create_axes(['x', 'y', 'z'])
mx = Mark(x, 20)
marks = []
slices = []
for i in range(10):
mz = Mark(z, i * 3 + 3)
marks.append(mz)
slices.append(fig.add((i / 5, i % 5), data, mz, x, y,
display_options=display_options))
vx = fig.add((0, 5), data, mx, y, z, shape=(2, 2),
display_options=display_options)
for m in marks:
from nisl import datasets, utils
from pynax import Figure, Mark, create_axes
import pylab as pl
import numpy as np
nyu = datasets.fetch_nyu_rest(n_subjects=1)
func = nyu.func[0]
niimg = utils.check_niimg(func)
fig = Figure((2, 7))
data = niimg.get_data()[..., 0]
# Awesome example activation map : take whatever is > .6 max
data_act = np.ma.MaskedArray(data, mask=(data < .6 * np.max(data)))
display_options = {}
display_options['interpolation'] = 'nearest'
display_options['cmap'] = pl.cm.gray
x, y, z = create_axes(['x', 'y', 'z'])
mx = Mark(x, 20)
marks = []
slices = []
for i in range(10):
mz = Mark(z, i * 3 + 3)
marks.append(mz)
slices.append(
fig.add((i / 5, i % 5),
data,
mz,
x,
y,
