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 _get_sensors(info, head_trans):
eeg_picks = mne.pick_types(info, meg=False, eeg=True, ref_meg=False)
eeg_loc = np.array([info['chs'][k]['loc'][:3] for k in eeg_picks])
eeg_loc = np.asfortranarray(apply_trans(head_trans, eeg_loc))
ch_names = [info['chs'][k]['ch_name'] for k in eeg_picks]
orientations = np.ones((len(ch_names), 3), order='F')
weights = np.ones(len(ch_names))
radii = np.ones(len(ch_names))
return om.Sensors(ch_names, eeg_loc, orientations, weights, radii),\
ch_names
def create_om_sensors(self, file_name=None):
"""
Take a TVB Sensors object and return an OpenMEEG Sensors object.
"""
if isinstance(self.sensors, sensors_module.SensorsEEG):
file_name = file_name or "eeg_sensors.txt"
sensors_file = self._tvb_eeg_sensors_to_txt(file_name)
elif isinstance(self.sensors, sensors_module.SensorsMEG):
file_name = file_name or "meg_sensors.squid"
sensors_file = self._tvb_meg_sensors_to_squid(file_name)
else:
LOG.error("sensors should be either SensorsEEG or SensorsMEG")
LOG.info("Wrote sensors to temporary file: %s" % str(file_name))
om_sensors = om.Sensors()
om_sensors.load(sensors_file)
return om_sensors
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 hm = om.HeadMat(geom, gauss_order) #hm.invert() # invert hm inplace (no copy) #hminv = hm
#
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
# TODO: mesh == [ double ] , [ int ]
# mesh = om.Mesh()
# mesh.load( file_name_skeleton + ".tri")
# TODO: V = [...]
# TODO: I = [...]
# TODO: mesh_1 = om.Mesh(V, I)
# TODO: geom.read() using raw python
# TODO: conductivity == { string => double}
# TODO: geometry == { [ mesh ] , { string => [ int ] } }
def _get_sensors_files(info, head_trans):
electrodes_fname = 'electrodes.txt'
ch_names = write_eeg_locations(electrodes_fname, info, head_trans)
eeg_electrodes = om.Sensors(electrodes_fname)
return eeg_electrodes, ch_names
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
dipole_in_cortex = True
#!/usr/bin/env python
import numpy as np
import openmeeg as om
# nes Sensors construtor
labels = ["toto"]
positions = np.array([[0, 1, 2], [0, 1, 2]], order='F')
orientations = np.array([[-1, -1, -2], [-1, -1, -2]], order='F')
weights = np.array([0.5, 0.5])
radii = np.array([1, 1])
s1 = om.Sensors(labels, positions, orientations, weights, radii)
print("s1 =", s1)
s1.info()