lava.lib.dnf.utils
lava.lib.dnf.utils.convenience
- lava.lib.dnf.utils.convenience.num_dims(shape)
Computes the dimensionality of a shape, assuming that (1,) represents a zero-dimensional shape.
- Parameters:
shape (tuple(int)) – shape of a population of neurons
- Returns:
number of dimensions
- Return type:
int
- lava.lib.dnf.utils.convenience.num_neurons(shape)
Computes the number of neurons from a shape.
- Return type:
int
Parameters:
- shapetuple(int)
shape of a neural population (or input)
Returns:
: num_neurons : int
number of neurons
- lava.lib.dnf.utils.convenience.to_ndarray(x)
Converts float, tuple, or list variables to numpy.ndarray.
- Parameters:
x (float, tuple, list, numpy.ndarray) – variable to convert
- Returns:
return – input converted to numpy.ndarray
- Return type:
numpy.ndarray
lava.lib.dnf.utils.math
- lava.lib.dnf.utils.math.gauss(shape, domain=None, amplitude=1.0, mean=None, stddev=None)
Evaluates the Gaussian function over a specified domain in multiple dimensions.
If a domain is specified, the function will evaluate the Gaussian at linearly interpolated values between the lower and upper bounds of the domain. The number of samples is determined by the shape parameter. For example, for given parameters shape=(5,) and domain=[[-5, -1]], it will evaluate the function at positions -5,-4,-3,-2,-1.
If no domain is specified, the function will evaluate the Gaussian at the indices of the sampling points. For instance, for a given shape of shape=(5,), it will evaluate the function at positions 0,1,2,3,4.
- Parameters:
shape (tuple(int)) – number of sampling points along each dimension
domain (numpy.ndarray, optional) – lower and upper bound of input values for each dimension at which the Gaussian function is evaluated
amplitude (float, optional) – amplitude of the Gaussian, defaults to 1
mean (numpy.ndarray, optional) – mean of the Gaussian, defaults to 0
stddev (numpy.ndarray, optional) – standard deviation of the Gaussian, defaults to 1
- Returns:
gaussian – multi-dimensional array with samples of the Gaussian
- Return type:
numpy.ndarray
- lava.lib.dnf.utils.math.is_odd(n)
Checks whether n is an odd number.
- Parameters:
n (int) – number to check
- Returns bool:
True if <n> is an odd number
- Return type:
bool
lava.lib.dnf.utils.plotting
- lava.lib.dnf.utils.plotting.compute_spike_rates(spike_data, window_size=11)
Computes instantaneous spike rates for all neurons over time
This method uses the window_size parameter to derive a kernel with which it convolves the spike_data. Yields an array of the same shape, with each value representing the spike rate of a neuron over the specified time window.
- Parameters:
spike_data (numpy.ndarray) – spike data of dtype=bool (spike: 1, no-spike: 0) and shape (num_time_steps, num_neurons)
window_size (int, optional) – size of the time window in number of time steps
- Returns:
spike_rates – array of same shape as spike_data which represents the instantaneous spike rate of every neuron at every time step
- Return type:
numpy.ndarray
- lava.lib.dnf.utils.plotting.raster_plot(spike_data, window_size=11)
Creates a raster plot, showing the spikes of all neurons over time.
The plot will use color to express the spike rate within a time window determined by rate_window (specified in number of time steps).
- Parameters:
spike_data (numpy.ndarray) – spike data of dtype=bool (spike: 1, no-spike: 0) and shape (num_time_steps, num_neurons)
window_size (int, optional) – size of the time window in number of time steps
- Return type:
None
lava.lib.dnf.utils.validation
- lava.lib.dnf.utils.validation.validate_shape(shape)
Validate and potentially convert shape parameter.
The shape of different elements of the DNF library can be passed in as type tuple(int) or list(int) for multiple dimensions, or type int for a single dimension. In all cases, it is converted to tuple(int).
- Return type:
Tuple[int,...]
Parameters:
- shapetuple(int) or list(int)
shape parameter to be validated
Returns:
: shape : tuple(int)
validated and converted shape parameter