neuroprob/likelihoods.py
def sample(self, rate, neuron=None, XZ=None):
        """
        Takes into account the quantization bias if we sample IPP with dilation factor.
        
        :param numpy.array rate: input rate of shape (trials, neuron, timestep)
        :returns: spike train of shape (trials, neuron, timesteps)
        :rtype: np.array
        """
        neuron = self._validate_neuron(neuron)
        return point_process.gen_IBP(rate[:, neuron, :]*self.tbin.item())

        Takes into account the quantization bias if we sample IPP with dilation factor.
        
        :param numpy.array rate: input rate of shape (trials, neuron, timestep)
        :returns: spike train of shape (trials, neuron, timesteps)
        :rtype: np.array
        
Takes into account the quantization bias if we sample IPP with dilation factor.
Takes into account the quantization bias if we sample IPP with dilation factor .
def sample(self, rate, neuron=None, XZ=None):
        neuron = self._validate_neuron(neuron)
        return point_process.gen_IBP(rate[:, neuron, :]*self.tbin.item())
def sample ( self , rate , neuron = None , XZ = None ) : neuron = self . _validate_neuron ( neuron ) return point_process . gen_IBP ( rate [ : , neuron , : ] * self . tbin . item ( ) )
Takes into account the quantization bias if we sample IPP with dilation factor.
Takes into account the quantization bias if we sample IPP with dilation factor .
"""
        Takes into account the quantization bias if we sample IPP with dilation factor.
        
        :param numpy.array rate: input rate of shape (trials, neuron, timestep)
        :returns: spike train of shape (trials, neuron, timesteps)
        :rtype: np.array
        """
spike train of shape (trials, neuron, timesteps)
spike train of shape ( trials neuron timesteps )
np.array
self
rate
input rate of shape (trials, neuron, timestep)
input rate of shape ( trials neuron timestep )
neuron
XZ