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)