def convolve_with_3d_basis(stim, basis, shape=['first', 'central', 'central']):
""" Project stimulus onto a basis.
:param stim T x Dx x Dy array of inputs.
T is the number of time bins
Dx is the stimulus x dimension.
Dy is the stimulus y dimension.
:param basis Tb x Dbx x Dby basis matrix
Tb is the length of the impulse response
Dbx is the basis x dimension
Dby is the basis y dimension
:rtype Tx1 vector of stimuli convolved with the 2D basis
"""
assert stim.ndim == basis.ndim == 3
(T, Dx, Dy) = stim.shape
(Tb, Dbx, Dby) = basis.shape
# First, by convention, the impulse responses are apply to times
# (t-R:t-1). That means we need to prepend a row of zeros to make
# sure the basis remains causal
basis = np.concatenate((np.zeros((1, Dbx, Dby)), basis), axis=0)
# Flip the spatial dimension for convolution
# We are convolving the stimulus with the filter, so the temporal part does
# NOT need to be flipped
basis = basis[:, ::-1, ::-1]
# Compute convolution using FFT
if Dx == Dbx and Dy == Dby and shape[1] == 'valid':
raise Warning("Use low rank convolution when D==Db!")
# Look for fft_stim in _fft_cache
fft_stim = None
for (cache_stim, cache_fft_stim) in _fft_cache:
if np.allclose(stim[-128:],cache_stim[-128:]) and \
np.allclose(stim[:128],cache_stim[:128]):
fft_stim = cache_fft_stim
break
if not fft_stim is None:
fstim, _ = fftconv.fftconvolve(stim, basis, 'full', fft_in1=fft_stim)
else:
fstim, fft_stim, _ = fftconv.fftconvolve(stim, basis, 'full')
_fft_cache.append((stim, fft_stim))
# Slice the result
assert len(shape) == 3
if shape[0] == 'first':
fstim = fstim[:T, :, :]
else:
raise Exception(
'Only supporting \'first\' slicing for dimension 0 (time)')
if shape[1] == 'full':
pass
elif shape[1] == 'central':
sz = Dx + Dbx - 1
start = (sz - Dx) / 2
stop = start + Dx
fstim = fstim[:, start:stop, :]
else:
raise NotImplementedError(
'Only supporting full and central slicing for spatial dims')
if shape[2] == 'full':
pass
elif shape[2] == 'central':
sz = Dy + Dby - 1
start = (sz - Dy) / 2
stop = start + Dy
fstim = fstim[:, :, start:stop]
else:
raise NotImplementedError(
'Only supporting full and central slicing for spatial dims')
return fstim
def convolve_with_3d_basis(stim, basis, shape=['first', 'central', 'central']):
""" Project stimulus onto a basis.
:param stim T x Dx x Dy array of inputs.
T is the number of time bins
Dx is the stimulus x dimension.
Dy is the stimulus y dimension.
:param basis Tb x Dbx x Dby basis matrix
Tb is the length of the impulse response
Dbx is the basis x dimension
Dby is the basis y dimension
:rtype Tx1 vector of stimuli convolved with the 2D basis
"""
assert stim.ndim == basis.ndim == 3
(T,Dx,Dy) = stim.shape
(Tb,Dbx,Dby) = basis.shape
# First, by convention, the impulse responses are apply to times
# (t-R:t-1). That means we need to prepend a row of zeros to make
# sure the basis remains causal
basis = np.concatenate((np.zeros((1,Dbx,Dby)),basis), axis=0)
# Flip the spatial dimension for convolution
# We are convolving the stimulus with the filter, so the temporal part does
# NOT need to be flipped
basis = basis[:,::-1, ::-1]
# Compute convolution using FFT
if Dx==Dbx and Dy==Dby and shape[1] == 'valid':
raise Warning("Use low rank convolution when D==Db!")
# Look for fft_stim in _fft_cache
fft_stim = None
for (cache_stim, cache_fft_stim) in _fft_cache:
if np.allclose(stim[-128:],cache_stim[-128:]) and \
np.allclose(stim[:128],cache_stim[:128]):
fft_stim = cache_fft_stim
break
if not fft_stim is None:
fstim,_ = fftconv.fftconvolve(stim, basis, 'full',
fft_in1=fft_stim)
else:
fstim,fft_stim,_ = fftconv.fftconvolve(stim, basis, 'full')
_fft_cache.append((stim,fft_stim))
# Slice the result
assert len(shape) == 3
if shape[0] == 'first':
fstim = fstim[:T,:,:]
else:
raise Exception('Only supporting \'first\' slicing for dimension 0 (time)')
if shape[1] == 'full':
pass
elif shape[1] == 'central':
sz = Dx + Dbx - 1
start = (sz - Dx)/2
stop = start + Dx
fstim = fstim[:,start:stop, :]
else:
raise NotImplementedError('Only supporting full and central slicing for spatial dims')
if shape[2] == 'full':
pass
elif shape[2] == 'central':
sz = Dy + Dby - 1
start = (sz - Dy)/2
stop = start + Dy
fstim = fstim[:,:,start:stop]
else:
raise NotImplementedError('Only supporting full and central slicing for spatial dims')
return fstim
def convolve_with_2d_basis(stim, basis, shape=['first', 'valid']):
""" Project stimulus onto a basis.
:param stim TxD matrix of inputs.
T is the number of time bins
D is the number of stimulus dimensions.
:param basis TbxDb basis matrix
Tb is the length of the impulse response
Db is the number of basis dimensions.
:rtype Tx1 vector of stimuli convolved with the 2D basis
"""
(T, D) = stim.shape
(Tb, Db) = basis.shape
# assert D==Db, "Spatial dimension of basis must match spatial dimension of stimulus."
# import scipy.signal as sig
# First, by convention, the impulse responses are apply to times
# (t-R:t-1). That means we need to prepend a row of zeros to make
# sure the basis remains causal
basis = np.vstack((np.zeros((1, Db)), basis))
# Flip the spatial dimension for convolution
# We are convolving the stimulus with the filter, so the temporal part does
# NOT need to be flipped
basis = basis[:, ::-1]
# Compute convolution using FFT
if D == Db and shape[1] == 'valid':
raise Warning("Use low rank convolution when D==Db!")
# Look for fft_stim in _fft_cache
fft_stim = None
for (cache_stim, cache_fft_stim) in _fft_cache:
if np.allclose(stim[-128:],cache_stim[-128:]) and \
np.allclose(stim[:128],cache_stim[:128]):
fft_stim = cache_fft_stim
break
if not fft_stim is None:
fstim, _ = fftconv.fftconvolve(stim, basis, 'full', fft_in1=fft_stim)
else:
fstim, fft_stim, _ = fftconv.fftconvolve(stim, basis, 'full')
_fft_cache.append((stim, fft_stim))
# Slice the result
assert len(shape) == 2
if shape[0] == 'first':
fstim = fstim[:T, :]
else:
raise Exception(
'Only supporting \'first\' slicing for dimension 0 (time)')
if shape[1] == 'valid':
assert Db == D, 'Dimension of basis must match that of stimuli for valid'
elif shape[1] == 'central':
sz = D + Db - 1
start = (sz - D) / 2
stop = start + D
fstim = fstim[:, start:stop]
return fstim
def convolve_with_2d_basis(stim, basis, shape=['first', 'valid']):
""" Project stimulus onto a basis.
:param stim TxD matrix of inputs.
T is the number of time bins
D is the number of stimulus dimensions.
:param basis TbxDb basis matrix
Tb is the length of the impulse response
Db is the number of basis dimensions.
:rtype Tx1 vector of stimuli convolved with the 2D basis
"""
(T,D) = stim.shape
(Tb,Db) = basis.shape
# assert D==Db, "Spatial dimension of basis must match spatial dimension of stimulus."
# import scipy.signal as sig
# First, by convention, the impulse responses are apply to times
# (t-R:t-1). That means we need to prepend a row of zeros to make
# sure the basis remains causal
basis = np.vstack((np.zeros((1,Db)),basis))
# Flip the spatial dimension for convolution
# We are convolving the stimulus with the filter, so the temporal part does
# NOT need to be flipped
basis = basis[:,::-1]
# Compute convolution using FFT
if D==Db and shape[1] == 'valid':
raise Warning("Use low rank convolution when D==Db!")
# Look for fft_stim in _fft_cache
fft_stim = None
for (cache_stim, cache_fft_stim) in _fft_cache:
if np.allclose(stim[-128:],cache_stim[-128:]) and \
np.allclose(stim[:128],cache_stim[:128]):
fft_stim = cache_fft_stim
break
if not fft_stim is None:
fstim,_ = fftconv.fftconvolve(stim, basis, 'full',
fft_in1=fft_stim)
else:
fstim,fft_stim,_ = fftconv.fftconvolve(stim, basis, 'full')
_fft_cache.append((stim,fft_stim))
# Slice the result
assert len(shape) == 2
if shape[0] == 'first':
fstim = fstim[:T,:]
else:
raise Exception('Only supporting \'first\' slicing for dimension 0 (time)')
if shape[1] == 'valid':
assert Db == D, 'Dimension of basis must match that of stimuli for valid'
elif shape[1] == 'central':
sz = D + Db - 1
start = (sz - D)/2
stop = start + D
fstim = fstim[:,start:stop]
return fstim
