def test_sensorsmeg(self):
dt = sensors.SensorsMEG()
dt.configure()
self.assertTrue(dt.has_orientation)
self.assertEqual(dt.labels.shape, (151,))
self.assertEqual(dt.locations.shape, (151, 3))
self.assertEqual(dt.number_of_sensors, 151)
self.assertEqual(dt.orientations.shape, (151, 3))
self.assertEqual(dt.sensors_type, 'MEG')
def test_sensorsmeg(self):
dt = sensors.SensorsMEG(load_default=True)
dt.configure()
self.assertTrue(isinstance(dt, sensors.SensorsMEG))
self.assertTrue(dt.has_orientation)
self.assertEqual(dt.labels.shape, (151, ))
self.assertEqual(dt.locations.shape, (151, 3))
self.assertEqual(dt.number_of_sensors, 151)
self.assertEqual(dt.orientations.shape, (151, 3))
self.assertEqual(dt.sensors_type, MEG_POLYMORPHIC_IDENTITY)
def test_sensorsmeg(self):
dt = sensors.SensorsMEG(load_file="meg_151.txt.bz2")
dt.configure()
assert isinstance(dt, sensors.SensorsMEG)
assert dt.has_orientation
assert dt.labels.shape == (151,)
assert dt.locations.shape == (151, 3)
assert dt.number_of_sensors == 151
assert dt.orientations.shape == (151, 3)
assert dt.sensors_type == MEG_POLYMORPHIC_IDENTITY
if __name__ == "__main__":
from tvb.tests.library import setup_test_console_env
setup_test_console_env()
# Third party python libraries
import numpy
import unittest
import itertools
# From "The Virtual Brain"
import tvb.datatypes.sensors as sensors
from tvb.tests.library.base_testcase import BaseTestCase
from tvb.simulator.lab import *
from tvb.basic.traits.parameters_factory import get_traited_subclasses
sens_meg = sensors.SensorsMEG()
sens_eeg = sensors.SensorsEEG()
class Simulator(object):
"""
Simulator test class
"""
def __init__(self):
"""
Initialise the structural information, coupling function, and monitors.
"""
#Initialise some Monitors with period in physical time
class MEG(Projection):
"Forward solution monitor for magnetoencephalography (MEG)."
_ui_name = "MEG"
projection = ProjectionSurfaceMEG(
default=None, label='Projection matrix', order=2,
doc='Projection matrix to apply to sources.')
sensors = sensors_module.SensorsMEG(
label = "MEG Sensors",
default = None,
required = True, order=1,
doc = """The set of MEG sensors for which the forward solution will be
calculated.""")
@classmethod
def from_file(cls, sensors_fname='meg_brainstorm_276.txt',
projection_fname='projection_meg_276_surface_16k.npy', **kwargs):
return Projection.from_file.im_func(cls, sensors_fname, projection_fname, **kwargs)
def analytic(self, loc, ori):
"""Compute single sphere analytic form of MEG lead field.
Equation 25 of [Sarvas_1987]_."""
# the magnetic constant = 1.25663706 × 10-6 m kg s-2 A-2 (H/m)
mu_0 = 1.25663706e-6 #mH/mm
# r => sensor positions
# r_0 => source positions
# a => vector from sources_to_sensor
# Q => source unit vectors
r_0, Q = loc, ori
centre = numpy.mean(r_0, axis=0)[numpy.newaxis, :]
radius = 1.01 * max(numpy.sqrt(numpy.sum((r_0 - centre)**2, axis=1)))
sensor_locations = self.sensors.locations.copy()
sen_dis = numpy.sqrt(numpy.sum((sensor_locations)**2, axis=1))
sensor_locations = sensor_locations / sen_dis[:, numpy.newaxis]
sensor_locations = sensor_locations * radius
sensor_locations = sensor_locations + centre
B_r = numpy.zeros((sensor_locations.shape[0], r_0.shape[0], 3))
for sensor_k in numpy.arange(sensor_locations.shape[0]):
a = sensor_locations[sensor_k,:] - r_0
na = numpy.sqrt(numpy.sum(a**2, axis=1))[:, numpy.newaxis]
rsk = sensor_locations[sensor_k,:][numpy.newaxis, :]
nr = numpy.sqrt(numpy.sum(rsk**2, axis=1))[:, numpy.newaxis]
F = a * (nr * a + nr**2 - numpy.sum(r_0 * rsk, axis=1)[:, numpy.newaxis])
adotr = numpy.sum((a / na) * rsk, axis=1)[:, numpy.newaxis]
delF = ((na**2 / nr + adotr + 2.0 * na + 2.0 * nr) * rsk -
(a + 2.0 * nr + adotr * r_0))
B_r[sensor_k, :] = ((mu_0 / (4.0 * numpy.pi * F**2)) *
(numpy.cross(F * Q, r_0) - numpy.sum(numpy.cross(Q, r_0) *
(rsk * delF), axis=1)[:, numpy.newaxis]))
return numpy.sqrt(numpy.sum(B_r**2, axis=2))
def create_time_series(self, storage_path, connectivity=None, surface=None,
region_map=None, region_volume_map=None):
return TimeSeriesMEG(storage_path=storage_path,
sensors=self.sensors,
sample_period=self.period,
title=' ' + self.__class__.__name__,
**self._transform_user_tags())
import os
import numpy
import unittest
import itertools
from tvb.simulator.common import get_logger
from tvb.simulator import simulator, models, coupling, integrators, monitors, noise
from tvb.datatypes import connectivity, sensors
from tvb.datatypes.cortex import Cortex
from tvb.datatypes.local_connectivity import LocalConnectivity
from tvb.datatypes.region_mapping import RegionMapping
from tvb.basic.traits.parameters_factory import get_traited_subclasses
from tvb.tests.library.base_testcase import BaseTestCase
LOG = get_logger(__name__)
sens_meg = sensors.SensorsMEG(load_default=True)
sens_eeg = sensors.SensorsEEG(load_default=True)
AVAILABLE_MODELS = get_traited_subclasses(models.Model)
AVAILABLE_METHODS = get_traited_subclasses(integrators.Integrator)
MODEL_CLASSES = AVAILABLE_MODELS.values()
METHOD_NAMES = AVAILABLE_METHODS.keys()
METHOD_NAMES.append('RungeKutta4thOrderDeterministic')
class Simulator(object):
"""
Simulator test class
"""
def __init__(self):
"""
