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4 changes: 2 additions & 2 deletions docs/source/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
"sphinx.ext.napoleon", # Unterstützt Google/NumPy-Style Docstrings
"sphinx.ext.mathjax",
"sphinx.ext.viewcode",
"sphinx.ext.intersphinx",
"sphinx.ext.intersphinx",
"myst_parser",
]

Expand Down Expand Up @@ -64,5 +64,5 @@
"navigation_depth": 5,
}
html_css_files = [
'custom.css',
"custom.css",
]
78 changes: 40 additions & 38 deletions src/pyGroupedTransforms/GroupedTransform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,14 +11,13 @@
"""

import numpy as np
import pykeops
import torch
from pykeops.torch import LazyTensor

from pyGroupedTransforms import *
from .NFFTtools import index_set_without_zeros

import pykeops
from pykeops.torch import LazyTensor

from .NFFTtools import index_set_without_zeros

# All code that is linked to NFMTtools or to system = "mixed" is not tested yet....

Expand Down Expand Up @@ -229,8 +228,8 @@ class GroupedTransform:
settings (list[Setting]): Pre-built settings. If empty, they are
derived from ``U``/``N`` or ``d``/``ds``/``N``.
algorithm (str, optional): Defaults to ``"nfft"``. ``"nfft"`` for the fast transforms, ``"keops"`` for implicit matrix multiplication (allow GPU or CPU computation),
or ``"direct"`` to build the matrix explicitly.
device: Device used by the ``"keops"`` algorithm, allows user to manually switch between GPU or CPU computation. Supported values are: ``"cuda"``
or ``"direct"`` to build the matrix explicitly.
device: Device used by the ``"keops"`` algorithm, allows user to manually switch between GPU or CPU computation. Supported values are: ``"cuda"``
for NVIDIA and CUDA-supporting devices, ``"mps"`` for Apple MPS-supporting devices, ``"cpu"`` for selecting CPU processing.
If ``None`` (default), the algorithm automatically selects the device based on availability, prioritizing GPU use.
parallel (bool): Run the sub-transforms threaded. Defaults to ``True``.
Expand Down Expand Up @@ -347,23 +346,22 @@ def __init__(
bandwidths=s.bandwidths, X=np.copy(X[:, u], order="C")
)
elif algorithm == "keops":
self.matrix = np.empty((0,0), dtype=object)
self.matrix = np.empty((0, 0), dtype=object)

if self.device:
device = device
else:
else:
if torch.cuda.is_available():
device = "cuda"
elif torch.mps.is_available():
device = "mps"
else:
device = "cpu"

self.transforms = [
DeferredLinearOperator()
for _ in range(len(self.settings))
DeferredLinearOperator() for _ in range(len(self.settings))
]

D = X.shape[1]

freq_list = []
Expand All @@ -372,8 +370,7 @@ def __init__(

if len(s.bandwidths) == 0:
full = np.zeros((1, D), dtype=np.int32)



local = np.atleast_2d(
s.mode.index_set_without_zeros(
np.array(s.bandwidths, dtype=np.int32)
Expand All @@ -388,13 +385,9 @@ def __init__(
freq_list.append(full)

freq = np.vstack(freq_list)

X_torch = torch.tensor(X, dtype=torch.float64, device=device)
I_torch = torch.tensor(
freq,
dtype=torch.float64,
device=device
)
I_torch = torch.tensor(freq, dtype=torch.float64, device=device)

def trafo(fhat):
if self.system == "cos" or self.system == "cheb":
Expand All @@ -403,8 +396,8 @@ def trafo(fhat):

kernel = 1.0
for i in range(D):
Xi = LazyTensor(X_torch[:, None, i:i+1].contiguous())
Ki = LazyTensor(I_torch[None, :, i:i+1].contiguous())
Xi = LazyTensor(X_torch[:, None, i : i + 1].contiguous())
Ki = LazyTensor(I_torch[None, :, i : i + 1].contiguous())
kernel = kernel * (2 * torch.pi * Xi * Ki).cos()

fhat_torch = (
Expand All @@ -428,7 +421,9 @@ def trafo(fhat):
two_pi_phase = -2 * torch.pi * phase_fwd
kernel = two_pi_phase.cos() + 1j * two_pi_phase.sin()

fhat_torch = torch.tensor(fhat, dtype=torch.complex128, device=device)
fhat_torch = torch.tensor(
fhat, dtype=torch.complex128, device=device
)
fhat_j = LazyTensor(fhat_torch[None, :, None].contiguous())

try:
Expand All @@ -443,15 +438,15 @@ def trafo(fhat):
def adjoint(f):
if self.system == "cos" or self.system == "cheb":
mult = np.sqrt(2.0) ** np.count_nonzero(freq, axis=1)

f_torch = torch.as_tensor(
f, dtype=torch.float64, device=device
).contiguous()

kernel = 1.0
for i in range(D):
Ki = LazyTensor(I_torch[:, None, i:i+1].contiguous())
Xi = LazyTensor(X_torch[None, :, i:i+1].contiguous())
Ki = LazyTensor(I_torch[:, None, i : i + 1].contiguous())
Xi = LazyTensor(X_torch[None, :, i : i + 1].contiguous())
kernel = kernel * (2 * torch.pi * Xi * Ki).cos()

f_i = LazyTensor(f_torch[None, :, None].contiguous())
Expand All @@ -465,7 +460,9 @@ def adjoint(f):
print("Error in KeOps adjoint:", e)
return None
else:
f_torch = torch.tensor(f, dtype=torch.complex128, device=device).contiguous()
f_torch = torch.tensor(
f, dtype=torch.complex128, device=device
).contiguous()

X_i = LazyTensor(X_torch[None, :, :].contiguous())
K_j = LazyTensor(I_torch[:, None, :].contiguous())
Expand All @@ -487,10 +484,15 @@ def adjoint(f):
return None

keops_dtype = np.float64 if self.system == "cos" else np.complex128
self.transforms = [DeferredLinearOperator(
dtype=keops_dtype, shape=(X.shape[0], len(freq)), mfunc=trafo, rmfunc=adjoint
)]

self.transforms = [
DeferredLinearOperator(
dtype=keops_dtype,
shape=(X.shape[0], len(freq)),
mfunc=trafo,
rmfunc=adjoint,
)
]

else:
self.transforms = []
s1 = self.settings[0]
Expand Down Expand Up @@ -576,15 +578,15 @@ def adjoint_worker(i):
adjoint_worker(i)

return fhat

elif self.algorithm == "keops":
return GroupedCoefficients(self.settings, self.transforms[0].H @ other)
return GroupedCoefficients(self.settings, self.transforms[0].H @ other)

elif self.algorithm == "direct":
return GroupedCoefficients(
self.settings, (self.matrix.conj()).T @ other
)

elif isinstance(other, GC): # `f = F*fhat` (fhat = other)
if self.settings != other.settings:
raise ValueError(
Expand Down Expand Up @@ -612,10 +614,10 @@ def worker(i):
worker(i)

return sum(results)

elif self.algorithm == "keops":
return self.transforms[0] @ other.data

elif self.algorithm == "direct":
return self.matrix @ other.data
else:
Expand Down
8 changes: 4 additions & 4 deletions src/pyGroupedTransforms/NFFTtools.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,10 @@
import numpy as np

from pyGroupedTransforms import *

import torch
import pykeops
import torch
from pykeops.torch import LazyTensor

from pyGroupedTransforms import *


def datalength(
bandwidths: np.ndarray,
Expand Down Expand Up @@ -173,6 +172,7 @@ def adjoint(f): # function adjoint(f::Vector{ComplexF64})::Vector{ComplexF64}
dtype=np.complex128, shape=(M, N), mfunc=trafo, rmfunc=adjoint
)


def get_matrix(
bandwidths, X
): # get_matrix(bandwidths::Vector{Int}, X::Array{Float64})::Array{ComplexF64}
Expand Down