def uirdftn(inarray, ndim=None, *args, **kwargs):
"""N-dim real unitary discrete Fourier transform
This transform consider the Hermitian property of the transform
from complex to real real input.
Parameters
----------
inarray : ndarray
The array to transform.
ndim : int, optional
The `ndim` last axis along wich to compute the transform. All
axes by default.
Returns
-------
outarray : array-like (the last ndim as (N - 1) * 2 lenght)
"""
if not ndim:
ndim = inarray.ndim
return dask_irfftn(inarray, axes=range(-ndim, 0), *args, **
kwargs) * da.sqrt(
da.prod(da.asarray(inarray.shape[-ndim:-1])) *
(inarray.shape[-1] - 1) * 2)
def estimate_log_joint_mark_intensity(decoding_marks,
encoding_marks,
mark_std,
place_bin_centers,
encoding_positions,
position_std,
occupancy,
mean_rate,
max_mark_value=6000,
position_distance=None):
"""
Parameters
----------
decoding_marks : ndarray, shape (n_decoding_spikes, n_features)
encoding_marks : ndarray, shape (n_encoding_spikes, n_features)
mark_std : float or ndarray, shape (n_features,)
place_bin_centers : ndarray, shape (n_position_bins, n_position_dims)
encoding_positions : ndarray, shape (n_decoding_spikes, n_position_dims)
position_std : float or ndarray, shape (n_position_dims,)
occupancy : ndarray, shape (n_position_bins,)
mean_rate : float
is_track_interior : None or ndarray, shape (n_position_bins,)
max_mark_value : int
set_diag_zero : bool
Returns
-------
log_joint_mark_intensity : ndarray, shape (n_decoding_spikes, n_position_bins)
"""
# mark_distance: ndarray, shape (n_decoding_spikes, n_encoding_spikes)
mark_distance = da.prod(
normal_pdf_integer_lookup(
np.expand_dims(decoding_marks, axis=1),
np.expand_dims(encoding_marks, axis=0),
std=mark_std,
max_value=max_mark_value
),
axis=-1
)
if decoding_marks is encoding_marks:
np.fill_diagonal(mark_distance, 0.0)
n_encoding_spikes = encoding_marks.shape[0]
if position_distance is None:
position_distance = estimate_position_distance(
place_bin_centers, encoding_positions, position_std)
return estimate_log_intensity(
mark_distance @ position_distance / n_encoding_spikes,
occupancy,
mean_rate)
