def test_mask_save():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
ds = cortex.Dataset(test=(np.random.randn(*volshape), subj, xfmname))
ds.append(masked=ds.test.masked['thin'])
data = ds.masked.data
ds.save(tf.name)
ds = cortex.load(tf.name)
assert ds.masked.shape == volshape
assert np.allclose(ds.masked.data, data)
def test_mask_save():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
ds = cortex.Dataset(test=(np.random.randn(*volshape), subj, xfmname))
ds.append(masked=ds.test.masked['thin'])
data = ds.masked.data
ds.save(tf.name)
ds = cortex.load(tf.name)
assert ds.masked.shape == volshape
assert np.allclose(ds.masked.data, data)
def test_pack():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
ds = cortex.Dataset(test=(np.random.randn(*volshape), subj, xfmname))
ds.save(tf.name, pack=True)
ds = cortex.load(tf.name)
pts, polys = cortex.db.get_surf(subj, "fiducial", "lh")
dpts, dpolys = ds.get_surf(subj, "fiducial", "lh")
assert np.allclose(pts, dpts)
rois = cortex.db.get_overlay(subj, "rois")
# Dataset.get_overlay returns a file handle, not an ROIpack ?
#assert rois.rois.keys() == ds.get_overlay(subj, "rois").rois.keys()
xfm = cortex.db.get_xfm(subj, xfmname)
assert np.allclose(xfm.xfm, ds.get_xfm(subj, xfmname).xfm)
def test_pack():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
ds = cortex.Dataset(test=(np.random.randn(*volshape), subj, xfmname))
ds.save(tf.name, pack=True)
ds = cortex.load(tf.name)
pts, polys = cortex.db.get_surf(subj, "fiducial", "lh")
dpts, dpolys = ds.get_surf(subj, "fiducial", "lh")
assert np.allclose(pts, dpts)
rois = cortex.db.get_overlay(subj, "rois")
# Dataset.get_overlay returns a file handle, not an ROIpack ?
#assert rois.rois.keys() == ds.get_overlay(subj, "rois").rois.keys()
xfm = cortex.db.get_xfm(subj, xfmname)
assert np.allclose(xfm.xfm, ds.get_xfm(subj, xfmname).xfm)
def test_dataset_save():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
mrand = np.random.randn(2, *volshape)
rand = np.random.randn(*volshape)
ds = cortex.Dataset(test=(mrand, subj, xfmname))
ds.append(twod=cortex.Volume2D(rand, rand, subj, xfmname))
ds.append(rgb=cortex.VolumeRGB(rand, rand, rand, subj, xfmname))
ds.append(vert=cortex.Vertex.random(subj))
ds.save(tf.name)
ds = cortex.load(tf.name)
assert isinstance(ds.test, cortex.Volume)
assert ds.test.data.shape == mrand.shape
assert isinstance(ds.twod, cortex.Volume2D)
assert ds.twod.dim1.data.shape == rand.shape
assert ds.twod.dim2.data.shape == rand.shape
assert ds.rgb.volume.shape == tuple([1] + list(volshape) + [4])
assert isinstance(ds.vert, cortex.Vertex)
def test_dataset_save():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
mrand = np.random.randn(2, *volshape)
rand = np.random.randn(*volshape)
ds = cortex.Dataset(test=(mrand, subj, xfmname))
ds.append(twod=cortex.Volume2D(rand, rand, subj, xfmname))
ds.append(rgb =cortex.VolumeRGB(rand, rand, rand, subj, xfmname))
ds.append(vert=cortex.Vertex.random(subj))
ds.save(tf.name)
ds = cortex.load(tf.name)
assert isinstance(ds.test, cortex.Volume)
assert ds.test.data.shape == mrand.shape
assert isinstance(ds.twod, cortex.Volume2D)
assert ds.twod.dim1.data.shape == rand.shape
assert ds.twod.dim2.data.shape == rand.shape
assert ds.rgb.volume.shape == tuple([1] + list(volshape) + [4])
assert isinstance(ds.vert, cortex.Vertex)
def test_pack():
tf = tempfile.NamedTemporaryFile(suffix=".hdf")
ds = cortex.Dataset(test=(np.random.randn(*volshape), subj, xfmname))
ds.save(tf.name, pack=True)
ds = cortex.load(tf.name)
pts, polys = cortex.db.get_surf(subj, "fiducial", "lh")
dpts, dpolys = ds.get_surf(subj, "fiducial", "lh")
assert np.allclose(pts, dpts)
overlay_db = cortex.db.get_overlay(subj, None, modify_svg_file=False)
rois_db = overlay_db.rois.labels.elements.keys()
# keep the temporary file object in memory to avoid the file being deleted
temp_file = ds.get_overlay(subj, "rois")
overlay_ds = cortex.db.get_overlay(subj,
temp_file.name,
modify_svg_file=False)
rois_ds = overlay_ds.rois.labels.elements.keys()
assert rois_db == rois_ds
xfm = cortex.db.get_xfm(subj, xfmname)
assert np.allclose(xfm.xfm, ds.get_xfm(subj, xfmname).xfm)
in a web viewer. In order for this demo to work, you need to download this
dataset_, but that can also be done automatically through the `urllib`
command that is included.
.. _dataset: http://gallantlab.org/pycortex/S1_retinotopy.hdf
S1 is the example subject that comes with pycortex, but if you want to plot
data onto a different subject, you will need to have them in your filestore.
This demo will not actually open the web viewer for you, but if you run it
yourself you will get a viewer showing something like the following.
.. image:: ../../webgl/angle_left.png
"""
import cortex
try: # python 2
from urllib import urlretrieve
except ImportError: # python 3
from urllib.request import urlretrieve
# Download and load in retinotopy data
_ = urlretrieve("http://gallantlab.org/pycortex/S1_retinotopy.hdf",
"S1_retinotopy.hdf")
ret_data = cortex.load("S1_retinotopy.hdf")
# Open the webviewer
cortex.webshow(ret_data)
S1 is the example subject that comes with pycortex, but if you want to plot
data onto a different subject, you will need to have them in your filestore,
and you will also need a flatmap for them.
"""
import six
import cortex
import matplotlib.pyplot as plt
if six.PY2:
from urllib import urlretrieve
elif six.PY3:
from urllib.request import urlretrieve
# Download the dataset and load it
_ = urlretrieve("http://gallantlab.org/pycortex/S1_retinotopy.hdf",
"S1_retinotopy.hdf")
ret_data = cortex.load("S1_retinotopy.hdf")
# The retinotopy data has to be divided into left and right hemispheres
left_data = ret_data.angle_left
cortex.quickshow(left_data,
with_curvature=True,
curvature_contrast=0.5,
curvature_brightness=0.5,
curvature_threshold=True)
plt.show()
right_data = ret_data.angle_right
cortex.quickshow(right_data,
with_curvature=True,
curvature_contrast=0.5,
curvature_brightness=0.5,