def __init__(self, source, target, parameters):
import pickle
ModularConnectorFunction.__init__(self, source, target, parameters)
t_size = target.size_in_degrees()
f = open(self.parameters.or_map_location, 'r')
mmap = pickle.load(f)
coords_x = numpy.linspace(-t_size[0] / 2.0, t_size[0] / 2.0,
numpy.shape(mmap)[0])
coords_y = numpy.linspace(-t_size[1] / 2.0, t_size[1] / 2.0,
numpy.shape(mmap)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
self.mmap = NearestNDInterpolator(zip(X.flatten(), Y.flatten()),
mmap.flatten())
self.or_source = self.mmap(
numpy.transpose(
numpy.array([
self.source.pop.positions[0], self.source.pop.positions[1]
]))) * numpy.pi
for (index, neuron2) in enumerate(target.pop.all()):
val_target = self.mmap(self.target.pop.positions[0][index],
self.target.pop.positions[1][index])
self.target.add_neuron_annotation(index,
'ORMapOrientation',
val_target * numpy.pi,
protected=False)
def __init__(self, source, target, parameters):
ModularConnectorFunction.__init__(self, source, target, parameters)
self.source_or = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentOrientation')
for i in xrange(0, self.source.pop.size)
])
self.source_phase = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentPhase')
for i in xrange(0, self.source.pop.size)
])
self.source_ar = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentAspectRatio')
for i in xrange(0, self.source.pop.size)
])
self.source_freq = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentFrequency')
for i in xrange(0, self.source.pop.size)
])
self.source_size = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentSize')
for i in xrange(0, self.source.pop.size)
])
self.source_posx = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentX')
for i in xrange(0, self.source.pop.size)
])
self.source_posy = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentY')
for i in xrange(0, self.source.pop.size)
])
def __init__(self, source, target, parameters):
ModularConnectorFunction.__init__(self, source, target, parameters)
self.source_or = numpy.array(
[self.source.get_neuron_annotation(i, "LGNAfferentOrientation") for i in xrange(0, self.source.pop.size)]
)
self.source_phase = numpy.array(
[self.source.get_neuron_annotation(i, "LGNAfferentPhase") for i in xrange(0, self.source.pop.size)]
)
def __init__(self, source,target, parameters):
ModularConnectorFunction.__init__(self, source,target, parameters)
self.source_or = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentOrientation') for i in xrange(0,self.source.pop.size)])
self.source_phase = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentPhase') for i in xrange(0,self.source.pop.size)])
self.source_ar = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentAspectRatio') for i in xrange(0,self.source.pop.size)])
self.source_freq = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentFrequency') for i in xrange(0,self.source.pop.size)])
self.source_size = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentSize') for i in xrange(0,self.source.pop.size)])
self.source_posx = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentX') for i in xrange(0,self.source.pop.size)])
self.source_posy = numpy.array([self.source.get_neuron_annotation(i, 'LGNAfferentY') for i in xrange(0,self.source.pop.size)])
def __init__(self, source, target, parameters):
ModularConnectorFunction.__init__(self, source, target, parameters)
self.source_or = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentOrientation')
for i in xrange(0, self.source.pop.size)
])
self.source_phase = numpy.array([
self.source.get_neuron_annotation(i, 'LGNAfferentPhase')
for i in xrange(0, self.source.pop.size)
])
def __init__(self, source,target, parameters):
import pickle
ModularConnectorFunction.__init__(self, source,target, parameters)
t_size = target.size_in_degrees()
f = open(self.parameters.map_location, 'r')
mmap = pickle.load(f)
coords_x = numpy.linspace(-t_size[0]/2.0,
t_size[0]/2.0,
numpy.shape(mmap)[0])
coords_y = numpy.linspace(-t_size[1]/2.0,
t_size[1]/2.0,
numpy.shape(mmap)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
self.mmap = NearestNDInterpolator(zip(X.flatten(), Y.flatten()),
mmap.flatten())
self.val_source=self.mmap(numpy.transpose(numpy.array([self.source.pop.positions[0],self.source.pop.positions[1]])))
def __init__(self, source, target, parameters):
import pickle
ModularConnectorFunction.__init__(self, source, target, parameters)
t_size = target.size_in_degrees()
f = open(self.parameters.map_location, 'r')
mmap = pickle.load(f)
coords_x = numpy.linspace(-t_size[0] / 2.0, t_size[0] / 2.0,
numpy.shape(mmap)[0])
coords_y = numpy.linspace(-t_size[1] / 2.0, t_size[1] / 2.0,
numpy.shape(mmap)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
self.mmap = NearestNDInterpolator(zip(X.flatten(), Y.flatten()),
mmap.flatten())
self.val_source = self.mmap(
numpy.transpose(
numpy.array([
self.source.pop.positions[0], self.source.pop.positions[1]
])))
def __init__(self, source, target, parameters):
import pickle
ModularConnectorFunction.__init__(self, source, target, parameters)
t_size = target.size_in_degrees()
f = open(self.parameters.map_location, "r")
mmap = pickle.load(f)
coords_x = numpy.linspace(-t_size[0] / 2.0, t_size[0] / 2.0, numpy.shape(mmap)[0])
coords_y = numpy.linspace(-t_size[1] / 2.0, t_size[1] / 2.0, numpy.shape(mmap)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
self.mmap = NearestNDInterpolator(zip(X.flatten(), Y.flatten()), mmap.flatten())
self.val_source = (
self.mmap(numpy.transpose(numpy.array([self.source.pop.positions[0], self.source.pop.positions[1]])))
* numpy.pi
)
for (index, neuron2) in enumerate(target.pop.all()):
val_target = self.mmap(self.target.pop.positions[0][index], self.target.pop.positions[1][index])
self.target.add_neuron_annotation(index, "LGNAfferentOrientation", val_target * numpy.pi, protected=False)
