def LFcomputing(condFile, geomFile, dipoleFile, electrodesFile, savedir):
"""
condFile = 'om_demo.cond'
geomFile = 'om_demo.geom'
dipoleFile = 'cortex.dip'
squidsFile = 'meg_squids.txt'
electrodesFile = 'eeg_electrodes.txt'
"""
# Load data
geom = om.Geometry()
geom.read(geomFile, condFile)
dipoles = om.Matrix()
dipoles.load(dipoleFile)
#squids = om.Sensors()
#squids.load(squidsFile)
electrodes = om.Sensors()
electrodes.load(electrodesFile)
# Compute forward problem
gaussOrder = 3
use_adaptive_integration = True
hm = om.HeadMat(geom, gaussOrder)
hminv = hm.inverse()
dsm = om.DipSourceMat(geom, dipoles, gaussOrder, use_adaptive_integration)
#ds2mm = om.DipSource2MEGMat (dipoles, squids)
#h2mm = om.Head2MEGMat (geom, squids)
h2em = om.Head2EEGMat(geom, electrodes)
#gain_meg = om.GainMEG (hminv, dsm, h2mm, ds2mm)
gain_eeg = om.GainEEG(hminv, dsm, h2em)
gain_eeg.save(savedir)
return gain_eeg
def _load_om_source_mat(self, file_name):
"""
Load a previously stored source matrix into an OpenMEEG Matrix object.
"""
source_matrix = om.Matrix()
source_matrix.load(file_name)
return source_matrix
def _get_dipoles(src, mri_trans, head_trans):
src_rr = np.r_[src[0]['rr'][src[0]['inuse'].astype(bool)],
src[1]['rr'][src[1]['inuse'].astype(bool)]]
if src[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI:
src_rr = apply_trans(mri_trans, src_rr)
# src_nn = apply_trans(mri_trans, src_nn, move=False)
elif src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD:
src_rr = apply_trans(head_trans, src_rr)
# src_nn = apply_trans(head_trans, src_nn, move=False)
pos = src_rr
ori = np.kron(np.ones(len(pos))[:, None], np.eye(3))
pos = np.kron(pos.T, np.ones(3)[None, :]).T
return om.Matrix(np.asfortranarray(np.c_[pos, ori]))
def create_om_sources(self): #TODO: Prob. should make file names specifiable
"""
Take a TVB Connectivity or Cortex object and return an OpenMEEG object
that specifies sources, a Matrix object for region level sources or a
Mesh object for a cortical surface source.
"""
if isinstance(self.sources, connectivity_module.Connectivity):
sources_file = self._tvb_connectivity_to_txt("sources.txt")
om_sources = om.Matrix()
elif isinstance(self.sources, Cortex):
sources_file = self._tvb_surface_to_tri("sources.tri")
om_sources = om.Mesh()
else:
LOG.error("sources must be either a Connectivity or Cortex.")
om_sources.load(sources_file)
return om_sources
subject = 'Head1' cond_file = op.join(data_path, subject, subject + '.cond') geom_file = op.join(data_path, subject, subject + '.geom') source_mesh_file = op.join(data_path, subject, subject + '.tri') dipole_file = op.join(data_path, subject, subject + '.dip') squidsFile = op.join(data_path, subject, subject + '.squids') patches_file = op.join(data_path, subject, subject + '.patches') geom = om.Geometry() geom.read(geom_file, cond_file) mesh = om.Mesh() mesh.load(source_mesh_file) dipoles = om.Matrix() dipoles.load(dipole_file) sensors = om.Sensors() sensors.load(squidsFile) patches = om.Sensors() patches.load(patches_file) ############################################################################### # Compute forward problem (Build Gain Matrices) gauss_order = 3 use_adaptive_integration = True dipole_in_cortex = True
#!/usr/bin/env python
import random
import numpy as np
import openmeeg as om
W = np.asfortranarray([[0.0, 0.0, 3.0], [0.0, 2.0, 0.0], [1.0, 0.0, 0.0]])
print("W of", W.__class__)
print("W =", W, "\n")
X = om.Matrix(W)
print("X of", X.__class__)
print("X =", X, "\n")
Z = X.array()
print("Z of", Z.__class__)
print("Z =", Z, "\n")
a = np.asfortranarray([[1, 2, 3], [4, 5, 6]])
print("a=", a)
b = om.Matrix(a)
assert b.nlin() == 2
assert b.ncol() == 3
for i in range(b.nlin()):
for j in range(b.ncol()):
assert b.value(i, j) == a[i, j]
c = om.Matrix(b)
assert c.nlin() == 2
assert c.ncol() == 3
"-p",
"--path",
dest="data_path",
help="path to data folder",
metavar="FILE",
default=data_path,
)
options, args = parser.parse_args()
data_path = options.data_path
#
subject = "Head1"
file_name_skeleton = op.join(data_path, subject, subject)
# dipole
dipoles = om.Matrix()
dipoles.load(file_name_skeleton + ".dip")
D = dipoles.array()
print("D is a", D.__class__)
print(D)
# Examples of basic linear algebra
print("Determinant of D is equal to: ", np.linalg.det(D))
# TODO: sensors.read() using numpy arrays
# TODO: sensors == [ double ]
sensors = om.Sensors()
sensors.load(file_name_skeleton + ".squids")
# TODO: D = asarray(sensors).copy...
# TODO: sensors_1 = om.Matrix(D)
# TODO: mesh.read() using numpy arrays
def _get_dipoles_files(src, mri_trans, head_trans):
dipoles_fname = 'dipoles.txt'
write_dipoles(dipoles_fname, src, mri_trans, head_trans)
dipoles = om.Matrix(dipoles_fname)
return dipoles
geom_file = path + 'data\\model\\head_model.geom'
cond_file = path + 'data\\model\\head_model.cond'
dipoles_file = path + 'data/model/cortex_dipoles.txt'
squids_file = path + 'data/model/meg_channels_locations.squids'
eeg_electrodes_file = path + 'data/model/eeg_channels_locations.txt'
eit_electrodes_file = path + 'data/model/eit_locations.txt'
ecog_electrodes_file = path + 'data/model/ecog_electrodes_locations.txt'
internal_electrodes_file = path + 'data/model/internal_electrodes_locations.txt'
print(geom_file)
geom = om.Geometry(geom_file, cond_file)
print(geom)
dipoles = om.Matrix(dipoles_file)
eeg_electrodes = om.Sensors(eeg_electrodes_file)
###############################################################################
# create a dir for leadfields and tmp
if not op.exists("tmp"):
import os
os.mkdir('tmp')
if not op.exists("leadfields"):
import os
os.mkdir('leadfields')
# Compute Leadfields
gauss_order = 3
use_adaptive_integration = True
print("W1 =", W1)
# np.array -> om.Vector
W2 = np.array([1.0, 2.0, 3.0])
print("W2 of", W2.__class__)
print("W2 =", W2, "\n")
V2 = om.Vector(W2, om.DEEP_COPY)
print("V2 of", V2.__class__)
V2.info()
V3 = om.Vector(V2)
print("V3 of", V3.__class__)
V3.info()
M = om.Matrix(2, 3)
M.setlin(0, V2)
M.set(3)
M.info()
print("M of", M.__class__)
print("V3 of", V3.__class__)
V3 = om.Vector(M)
print("V3 of", V3.__class__)
V3.info()
#
error = 0
for i in range(3):
if W1[i] != V1.value(i):
print("vectors differ at:", i)
