from nisl import datasets, _utils
from pynax.core import Mark
from pynax.view import ImshowView
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 = pl.figure(figsize=(17, 5))
data = niimg.get_data()[..., 0]
# Awesome example activation map: threshold
data_act = np.ma.MaskedArray(data * 0.8, mask=(data < .6 * np.max(data)))
# Display options
display_options = {}
display_options['interpolation'] = 'nearest'
display_options['cmap'] = pl.cm.gray
ac_display_options = {}
ac_display_options['interpolation'] = 'nearest'
ac_display_options['cmap'] = pl.cm.autumn
ac_display_options['vmin'] = data_act.min()
ac_display_options['vmax'] = data_act.max()
# Marks
mx = Mark(20, {'color': 'r'})
marks = []
views = []
for i in range(10):
"""
Simple example of NiftiMasker use
==================================
Here is a simple example of automatic mask computation using the nifti masker.
The mask is computed and visualized.
"""
### Load nyu_rest dataset #####################################################
from nisl import datasets
from nisl.io import NiftiMasker
dataset = datasets.fetch_nyu_rest(n_subjects=1)
### Compute the mask ##########################################################
nifti_masker = NiftiMasker(memory="nisl_cache", memory_level=2)
nifti_masker.fit(dataset.func[0])
mask = nifti_masker.mask_img_.get_data()
### Visualize the mask ########################################################
import pylab as pl
import numpy as np
import nibabel
pl.figure()
pl.axis('off')
pl.imshow(np.rot90(nibabel.load(dataset.func[0]).get_data()[..., 20, 0]),
interpolation='nearest', cmap=pl.cm.gray)
ma = np.ma.masked_equal(mask, False)
pl.imshow(np.rot90(ma[..., 20]), interpolation='nearest', cmap=pl.cm.autumn,
alpha=0.5)
