def test_overlay():
"""Test plotting of overlay
"""
mlab.options.backend = 'test'
# basic overlay support
overlay_file = pjoin(data_dir, "lh.sig.nii.gz")
brain = Brain(*std_args)
brain.add_overlay(overlay_file)
brain.overlays["sig"].remove()
brain.add_overlay(overlay_file, min=5, max=20, sign="pos")
sig1 = io.read_scalar_data(pjoin(data_dir, "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(pjoin(data_dir, "lh.alt_sig.nii.gz"))
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
brain.add_overlay(sig1, 4, 30, name="sig1")
brain.overlays["sig1"].pos_bar.lut_mode = "Reds"
brain.overlays["sig1"].pos_bar.visible = False
brain.add_overlay(sig2, 4, 30, name="sig2")
brain.overlays["sig2"].pos_bar.lut_mode = "Blues"
brain.overlays["sig2"].pos_bar.visible = False
brain.add_overlay(conjunct, 4, 30, name="conjunct")
brain.overlays["conjunct"].pos_bar.lut_mode = "Purples"
brain.overlays["conjunct"].pos_bar.visible = False
brain.close()
def test_overlay():
"""Test plotting of overlay
"""
mlab.options.backend = 'test'
# basic overlay support
overlay_file = pjoin(data_dir, "lh.sig.nii.gz")
brain = Brain(*std_args)
brain.add_overlay(overlay_file)
brain.overlays["sig"].remove()
brain.add_overlay(overlay_file, min=5, max=20, sign="pos")
sig1 = io.read_scalar_data(pjoin(data_dir, "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(pjoin(data_dir, "lh.alt_sig.nii.gz"))
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
brain.add_overlay(sig1, 4, 30, name="sig1")
brain.overlays["sig1"].pos_bar.lut_mode = "Reds"
brain.overlays["sig1"].pos_bar.visible = False
brain.add_overlay(sig2, 4, 30, name="sig2")
brain.overlays["sig2"].pos_bar.lut_mode = "Blues"
brain.overlays["sig2"].pos_bar.visible = False
brain.add_overlay(conjunct, 4, 30, name="conjunct")
brain.overlays["conjunct"].pos_bar.lut_mode = "Purples"
brain.overlays["conjunct"].pos_bar.visible = False
brain.close()
def vizify(self):
for hemi in self.hemis:
print "visualize %s" % hemi
# Bring up the beauty (the underlay)
brain = Brain(self.subject_id, hemi, self.surf, \
config_opts=self.config_opts, \
subjects_dir=self.subjects_dir)
surf_data = io.read_scalar_data(self.overlay_surf[hemi])
if (sum(abs(surf_data)) > 0):
# Overlay another hopeful beauty (functional overlay)
brain.add_overlay(self.overlay_surf[hemi], name=self.overlay_name,
min=self.min, max=self.max, sign=self.sign)
# Update colorbar
#brain.overlays[self.overlay_name].pos_bar.lut_mode = self.colorbar
tmp = brain.overlays[self.overlay_name]
lut = tmp.pos_bar.lut.table.to_array()
lut[:,0:3] = self.colorbar
tmp.pos_bar.lut.table = lut
# Refresh
brain.show_view("lat")
brain.hide_colorbar()
# Save the beauts
brain.save_imageset("%s_%s" % (self.outprefix, hemi), self.views,
'jpg', colorbar=None)
# End a great journey, till another life
brain.close()
return
def loadScalar(pathToScalar):
'''
Method to read scalar vectors for example in mgh format
'''
outVec = io.read_scalar_data(pathToScalar)
return outVec
def vol_to_surf(infile, outprefix=None, interp="trilinear", hemis=["lh", "rh"]):
"""
Transforms a volume from MNI to FSaverage space using mri_vol2surf.
Parameters
----------
infile : str
outprefix : str or None (default: None)
When outfile is None, the path will be autogenerated using tmpfile.
The suffix of the file will be the hemi and .nii.gz.
interp : str (default: trilinear)
Can be 'nearest' or 'trilinear'.
hemis : list (default: ['lh', 'rh'])
Returns
--------
outfiles : dict
For instance: `{'lh': 'file1', 'rh': 'file2'}`.
outsurfs : dict
For instance: `{'lh': surf_lh, 'rh': surf_rh}` where surf_* is an
`ndarray`.
"""
if not op.exists(infile):
raise Exception("Input for vol_to_surf '%s' doesn't exist" % infile)
if outprefix is None:
to_clean = True
_,outprefix = tempfile.mkstemp()
else:
to_clean = False
outfiles = {}
outsurfs = {}
templ_vars = dict(input=infile, interp=interp)
for hemi in hemis:
outfile = "%s_%s.nii.gz" % (outprefix, hemi)
outfiles[hemi] = outfile
templ_vars["hemi"] = hemi
templ_vars["output"] = outfile
cmd = "mri_vol2surf --mov %(input)s \
--mni152reg --projfrac-max 0 1 0.1 \
--interp %(interp)s --hemi %(hemi)s \
--out %(output)s --reshape" % templ_vars
print cmd
p = Process(cmd)
print p.stdout
print p.stderr
if p.retcode != 0:
raise Exception("mri_vol2surf failed")
outsurfs[hemi] = io.read_scalar_data(outfile)
if to_clean:
os.remove(outprefix)
return (outfiles, outsurfs)
def test_overlay():
"""Test plotting of overlay."""
_set_backend()
# basic overlay support
overlay_file = pjoin(data_dir, "lh.sig.nii.gz")
brain = Brain(*std_args)
brain.add_overlay(overlay_file)
brain.overlays["sig"].remove()
brain.add_overlay(overlay_file, min=5, max=20, sign="pos")
sig1 = io.read_scalar_data(pjoin(data_dir, "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(pjoin(data_dir, "lh.alt_sig.nii.gz"))
# two-sided overlay
brain.add_overlay(sig1, 4, 30, name="two-sided")
overlay = brain.overlays_dict.pop('two-sided')[0]
assert_array_equal(overlay.pos_bar.data_range, [4, 30])
assert_array_equal(overlay.neg_bar.data_range, [-30, -4])
assert_equal(overlay.pos_bar.reverse_lut, True)
assert_equal(overlay.neg_bar.reverse_lut, False)
overlay.remove()
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
brain.add_overlay(sig1, 4, 30, name="sig1")
brain.overlays["sig1"].pos_bar.lut_mode = "Reds"
brain.overlays["sig1"].pos_bar.visible = False
brain.add_overlay(sig2, 4, 30, name="sig2")
brain.overlays["sig2"].pos_bar.lut_mode = "Blues"
brain.overlays["sig2"].pos_bar.visible = False
brain.add_overlay(conjunct, 4, 30, name="conjunct")
brain.overlays["conjunct"].pos_bar.lut_mode = "Purples"
brain.overlays["conjunct"].pos_bar.visible = False
brain.set_surf('white')
brain.close()
def test_overlay():
"""Test plotting of overlay."""
_set_backend()
# basic overlay support
overlay_file = pjoin(data_dir, "lh.sig.nii.gz")
brain = Brain(*std_args)
brain.add_overlay(overlay_file)
brain.overlays["sig"].remove()
brain.add_overlay(overlay_file, min=5, max=20, sign="pos")
sig1 = io.read_scalar_data(pjoin(data_dir, "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(pjoin(data_dir, "lh.alt_sig.nii.gz"))
# two-sided overlay
brain.add_overlay(sig1, 4, 30, name="two-sided")
overlay = brain.overlays_dict.pop('two-sided')[0]
assert_array_equal(overlay.pos_bar.data_range, [4, 30])
assert_array_equal(overlay.neg_bar.data_range, [-30, -4])
assert_equal(overlay.pos_bar.reverse_lut, True)
assert_equal(overlay.neg_bar.reverse_lut, False)
overlay.remove()
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
brain.add_overlay(sig1, 4, 30, name="sig1")
brain.overlays["sig1"].pos_bar.lut_mode = "Reds"
brain.overlays["sig1"].pos_bar.visible = False
brain.add_overlay(sig2, 4, 30, name="sig2")
brain.overlays["sig2"].pos_bar.lut_mode = "Blues"
brain.overlays["sig2"].pos_bar.visible = False
brain.add_overlay(conjunct, 4, 30, name="conjunct")
brain.overlays["conjunct"].pos_bar.lut_mode = "Purples"
brain.overlays["conjunct"].pos_bar.visible = False
brain.set_surf('white')
brain.close()
def mask_vtx_data(overlay_fname, cortex_fname, thresh):
vtx_data = io.read_scalar_data(overlay_fname)
cortex_data = io.read_label(cortex_fname)
# Create a mask of 1s where there is cortex and 0s on the medial wall
mask = np.zeros_like(vtx_data)
mask[cortex_data] = 1
# Set all values that are not in cortex to thresh-1
vtx_data[mask == 0] = thresh-1
return vtx_data
def mask_vtx_data(overlay_fname, cortex_fname, thresh):
vtx_data = io.read_scalar_data(overlay_fname)
cortex_data = io.read_label(cortex_fname)
# Create a mask of 1s where there is cortex and 0s on the medial wall
mask = np.zeros_like(vtx_data)
mask[cortex_data] = 1
# Set all values that are not in cortex to thresh-1
vtx_data[mask == 0] = thresh-1
return vtx_data
def vizify(self):
for hemi in self.hemis:
print "visualize %s" % hemi
# Bring up the beauty (the underlay)
brain = Brain(self.subject_id, hemi, self.surf, \
config_opts=self.config_opts, \
subjects_dir=self.subjects_dir)
surf_data = io.read_scalar_data(self.overlay_surf[hemi])
if (sum(abs(surf_data)) > 0):
# Overlay another hopeful beauty (functional overlay)
brain.add_overlay(self.overlay_surf[hemi],
name=self.overlay_name,
min=self.min,
max=self.max,
sign=self.sign)
# Update colorbar
#brain.overlays[self.overlay_name].pos_bar.lut_mode = self.colorbar
tmp = brain.overlays[self.overlay_name]
lut = tmp.pos_bar.lut.table.to_array()
lut[:, 0:3] = self.colorbar
tmp.pos_bar.lut.table = lut
# Refresh
brain.show_view("lat")
brain.hide_colorbar()
# Save the beauts
brain.save_imageset("%s_%s" % (self.outprefix, hemi),
self.views,
'jpg',
colorbar=None)
# End a great journey, till another life
brain.close()
return
###
def run(cmd):
print(cmd)
os.system(cmd)
prefix = op.join(odir, "surf_neurosynth")
ns_combined_dir = op.join(base,
"neurosynth/neurosynth/intelligence_reasoning_wm")
cmd = "./x_vol2surf.py %s/_pAgF_z.nii.gz %s/_pAgF_z_FDR_0.05.nii.gz %s" % (
ns_combined_dir, ns_combined_dir, prefix)
#run(cmd)
ns_combined = {
hemi: io.read_scalar_data("%s_%s.nii.gz" % (prefix, hemi))
for hemi in ["lh", "rh"]
}
all_ns = ns_combined.values()
print 'getting range'
dmin2 = np.array([ns[ns.nonzero()].min() for ns in all_ns]).min()
dmax2 = np.array([ns[ns.nonzero()].max() for ns in all_ns]).max()
print 'min=%.4f; max=%.4f' % (dmin2, dmax2)
###
# Overlap
###
scans_and_ns = {}
for hemi in ["lh", "rh"]:
print 'min=%.4f; max=%.4f' % (dmin,dmax)
###
# Neurosynth
###
def run(cmd):
print(cmd)
os.system(cmd)
prefix = op.join(odir, "surf_neurosynth")
ns_combined_dir = op.join(base, "neurosynth/neurosynth/intelligence_reasoning_wm")
cmd = "./x_vol2surf.py %s/_pAgF_z.nii.gz %s/_pAgF_z_FDR_0.05.nii.gz %s" % (ns_combined_dir, ns_combined_dir, prefix)
#run(cmd)
ns_combined = { hemi : io.read_scalar_data("%s_%s.nii.gz" % (prefix, hemi)) for hemi in ["lh", "rh"] }
all_ns = ns_combined.values()
print 'getting range'
dmin2 = np.array([ ns[ns.nonzero()].min() for ns in all_ns ]).min()
dmax2 = np.array([ ns[ns.nonzero()].max() for ns in all_ns ]).max()
print 'min=%.4f; max=%.4f' % (dmin2,dmax2)
###
# Overlap
###
scans_and_ns = {}
for hemi in ["lh", "rh"]:
# 1. Combine IQ CWAS Scan 1 and 2
dmin = ranges.min()
dmax = ranges.max()
print 'min=%.4f; max=%.4f' % (dmin,dmax)
###
# Read surfaces
###
print "...reading surfaces"
surfs = {}
for measure in measures:
surfs[measure] = {}
for hemi in ["lh", "rh"]:
tmp = io.read_scalar_data(all_surf_files[measure][hemi])
surfs[measure][hemi] = tmp
###
# Plot
###
print "...plotting"
cbarfile = "/home2/data/Projects/CWAS/share/lib/surfwrap/colorbars/red-yellow.txt"
cbar = load_colorbar(cbarfile)
# Overlap
for measure in measures:
oprefix = op.join(odir, "surf_%s" % measure)
ipython
#%gui qt
%gui wx
from surfer import Brain
import mayavi
from numpy import array
import os.path as op
import numpy as np
from surfer import io
########################## TJ - SJ #########################"
brain = Brain("fsaverage", "lh", "pial", config_opts={"cortex":"low_contrast","background":"black"})
sig1 = np.nan_to_num(io.read_scalar_data("/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/fMRI/GROUPlvl_TJ-SJ_lh.mgz"))
sig2 = np.nan_to_num(io.read_scalar_data('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/fMRI/GROUPlvl_SJ-TJ_lh.mgz'))
thresh = 1
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
brain.add_overlay(sig1, 1, 8, name="sig1")
brain.overlays["sig1"].pos_bar.lut_mode = "Reds"
brain.overlays["sig1"].pos_bar.visible = False
brain.add_overlay(sig2, 1, 8, name="sig2")
brain.overlays["sig2"].pos_bar.lut_mode = "Blues"
brain.overlays["sig2"].pos_bar.visible = False
################### CONJ[TJ-CTRL SJ- CTRL] #####################
################## WHITE #######################################
brain.add_overlay(overlay_file, min=3.11, max=10, sign="pos")
mayavi.mlab.view(azimuth=225, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_TJ_CTRL_tT_extT_lh_projfrac05_face1.png", size=None, figure=None, magnification='auto')
mayavi.mlab.view(azimuth=45, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_TJ_CTRL_tT_extT_rh_projfrac05_face2.png", size=None, figure=None, magnification='auto')
mayavi.mlab.view(azimuth=315, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_TJ_CTRL_tT_extT_rh_projfrac05_face3.png", size=None, figure=None, magnification='auto')
################### SJ-CTRL #####################
###################### LH #######################
brain = Brain("fsaverage", "lh", "inflated", config_opts={"cortex":"low_contrast","background":"black"})
overlay_file = "/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/fMRI/GROUPlvl_SJ_CTRL_tT_extT_lh_projfrac05.mgz"
brain.add_overlay(overlay_file, min=3.11, max=10, sign="pos")
sig1 = io.read_scalar_data(overlay_file)
brain.add_overlay(sig1, 3.11, 10, name="sig1")
brain.overlays['sig1'].pos_bar.lut_mode = "Blues"
mayavi.mlab.view(azimuth=315, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_SJ_CTRL_tT_extT_lh_projfrac05_face1.png", size=None, figure=None, magnification='auto')
mayavi.mlab.view(azimuth=135, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_SJ_CTRL_tT_extT_lh_projfrac05_face2.png", size=None, figure=None, magnification='auto')
mayavi.mlab.view(azimuth=225, elevation=None, distance=None, focalpoint=None,
roll=None, reset_roll=True, figure=None)
mayavi.mlab.savefig("/neurospin/unicog/protocols/IRMf/BENOIT_MT/BAPTISTE_FOLDER/PICTURES/MOD30-2/GROUPlvl_SJ_CTRL_tT_extT_lh_projfrac05_face3.png", size=None, figure=None, magnification='auto')
###################### RH #######################
brain = Brain("fsaverage", "rh", "inflated", config_opts={"cortex":"low_contrast","background":"black"})
overlay_file = "/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/fMRI/GROUPlvl_SJ_CTRL_tT_extT_rh_projfrac05.mgz"
brain.add_overlay(overlay_file, min=3.11, max=10, sign="pos")
import numpy as np
from surfer import io
from surfer import Brain
print(__doc__)
"""
Initialize the visualization.
"""
brain = Brain("fsaverage", "lh", "inflated", background="white")
"""
Read both of the activation maps in using
surfer's io functions.
"""
sig1 = io.read_scalar_data(op.join('example_data', "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(op.join('example_data', "lh.alt_sig.nii.gz"))
"""
Zero out the vertices that do not meet a threshold.
"""
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
"""
A conjunction is defined as the minimum significance
value between the two maps at each vertex.
"""
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
ranges = np.array([ get_range(sfile) for sfile in all_sfiles ])
dmin = ranges.min()
dmax = ranges.max()
print 'min=%.4f; max=%.4f' % (dmin,dmax)
###
# Overlap
###
no = {}
yes = {}
combined = {}
for hemi in ["lh", "rh"]:
no[hemi] = io.read_scalar_data(all_surf_files["no"][hemi])
yes[hemi] = io.read_scalar_data(all_surf_files["yes"][hemi])
combined[hemi] = np.min(np.vstack((no[hemi], yes[hemi])), axis=0)
###
# Plot
###
# Overlap
oprefix = op.join(odir, "surface_compare_transformation")
for hemi in ["lh","rh"]:
brain = fsaverage(hemi)
surfov = no[hemi]
def vol_to_surf(infile,
outprefix=None,
interp="trilinear",
hemis=["lh", "rh"]):
"""
Transforms a volume from MNI to FSaverage space using mri_vol2surf.
Parameters
----------
infile : str
outprefix : str or None (default: None)
When outfile is None, the path will be autogenerated using tmpfile.
The suffix of the file will be the hemi and .nii.gz.
interp : str (default: trilinear)
Can be 'nearest' or 'trilinear'.
hemis : list (default: ['lh', 'rh'])
Returns
--------
outfiles : dict
For instance: `{'lh': 'file1', 'rh': 'file2'}`.
outsurfs : dict
For instance: `{'lh': surf_lh, 'rh': surf_rh}` where surf_* is an
`ndarray`.
"""
if not op.exists(infile):
raise Exception("Input for vol_to_surf '%s' doesn't exist" % infile)
if outprefix is None:
to_clean = True
_, outprefix = tempfile.mkstemp()
else:
to_clean = False
outfiles = {}
outsurfs = {}
templ_vars = dict(input=infile, interp=interp)
for hemi in hemis:
outfile = "%s_%s.nii.gz" % (outprefix, hemi)
outfiles[hemi] = outfile
templ_vars["hemi"] = hemi
templ_vars["output"] = outfile
cmd = "mri_vol2surf --mov %(input)s \
--mni152reg --projfrac-max 0 1 0.1 \
--interp %(interp)s --hemi %(hemi)s \
--out %(output)s --reshape" % templ_vars
print cmd
p = Process(cmd)
print p.stdout
print p.stderr
if p.retcode != 0:
raise Exception("mri_vol2surf failed")
outsurfs[hemi] = io.read_scalar_data(outfile)
if to_clean:
os.remove(outprefix)
return (outfiles, outsurfs)
easydir = easydirs[scan]
surf_files = {
"lh": op.join(easydir, "surf_lh_thresh_zstat_FSIQ.nii.gz"),
"rh": op.join(easydir, "surf_rh_thresh_zstat_FSIQ.nii.gz")
}
ba_files = {
"lh": "/home2/data/PublicProgram/freesurfer/fsaverage_copy/label/lh.PALS_B12_Brodmann.annot",
"rh": "/home2/data/PublicProgram/freesurfer/fsaverage_copy/label/rh.PALS_B12_Brodmann.annot",
}
hemi = "lh"
ba = io.read_annot(ba_files[hemi])
cwas = io.read_scalar_data(surf_files[hemi])
rois = ba[0]
urois = np.unique(rois); urois.sort()
labels = np.array(ba[2])[urois]
cols = ["index", "roi", "ba", "summary_wt", "summary_uwt", "meta_analysis"]
dict_df = { k : [] for k in cols }
for i,label in enumerate(labels):
if label.find("Brodmann") == -1:
continue
ba = int(label[9:])
roi = urois[i]
summary_wt = cwas[rois==roi].mean()
dmax = ranges.max()
print 'min=%.4f; max=%.4f' % (dmin,dmax)
###
###
# Neurosynth
###
def run(cmd):
print(cmd)
os.system(cmd)
prefix = "/home2/data/Projects/CWAS/results/20_cwas_iq/12_iq_surface/30_neurosynth_iq_surf_thresh"
neurosynth = { hemi : io.read_scalar_data("%s_%s.nii.gz" % (prefix, hemi)) for hemi in ["lh", "rh"] }
all_ns = neurosynth.values()
prefix = op.join(odir, "surfs", "surf_ns_intelligence_all")
ns_orig_dir = op.join(base, "neurosynth/neurosynth/intelligence_001")
cmd = "./x_vol2surf.py %s/_pAgF_z.nii.gz %s/_pAgF_z_FDR_0.05.nii.gz %s" % (ns_orig_dir, ns_orig_dir, prefix)
run(cmd)
ns_orig = { hemi : io.read_scalar_data("%s_%s.nii.gz" % (prefix, hemi)) for hemi in ["lh", "rh"] }
all_ns = ns_orig.values()
prefix = "surf_ns_intelligence_fmri"
ns_fmri_dir = op.join(base, "neurosynth/neurosynth/intelligence_very_relevant_fmri")
cmd = "./x_vol2surf.py %s/_pAgF_z.nii.gz %s/_pAgF_z_FDR_0.05.nii.gz %s" % (ns_fmri_dir, ns_fmri_dir, prefix)
run(cmd)
ns_fmri = { hemi : io.read_scalar_data("%s_%s.nii.gz" % (prefix, hemi)) for hemi in ["lh", "rh"] }
all_ns = ns_fmri.values()
import numpy as np
from surfer import io
from surfer import Brain
"""
Initialize the visualization.
"""
brain = Brain("fsaverage", "lh", "inflated",
config_opts=dict(background="white"))
"""
Read both of the activation maps in using
surfer's io functions.
"""
data_dir = op.join("example_data")
sig1 = io.read_scalar_data(op.join(data_dir, "lh.sig.nii.gz"))
sig2 = io.read_scalar_data(op.join(data_dir, "lh.alt_sig.nii.gz"))
"""
Zero out the vertices that do not meet a threshold.
"""
thresh = 4
sig1[sig1 < thresh] = 0
sig2[sig2 < thresh] = 0
"""
A conjunction is defined as the minimum significance
value between the two maps at each vertex.
"""
conjunct = np.min(np.vstack((sig1, sig2)), axis=0)
print 'getting range'
ranges = np.array([get_range(sfile) for sfile in all_sfiles])
dmin = ranges.min()
dmax = ranges.max()
print 'min=%.4f; max=%.4f' % (dmin, dmax)
###
# Read surfaces
###
print "...reading surfaces"
surfs = {}
for measure in measures:
surfs[measure] = {}
for hemi in ["lh", "rh"]:
tmp = io.read_scalar_data(all_surf_files[measure][hemi])
surfs[measure][hemi] = tmp
###
# Plot
###
print "...plotting"
cbarfile = "/home2/data/Projects/CWAS/share/lib/surfwrap/colorbars/red-yellow.txt"
cbar = load_colorbar(cbarfile)
# Overlap
for measure in measures:
oprefix = op.join(odir, "surf_%s" % measure)
for hemi in ["lh", "rh"]:
