From 88ec956339669537820ceab522ae86899fed6fd8 Mon Sep 17 00:00:00 2001 From: texlucas Date: Thu, 16 Jul 2026 10:55:42 +0000 Subject: [PATCH] Auto code format --- src/pyGroupedTransforms/GroupedTransform.py | 72 +++++++++++---------- src/pyGroupedTransforms/NFFTtools.py | 8 +-- 2 files changed, 41 insertions(+), 39 deletions(-) diff --git a/src/pyGroupedTransforms/GroupedTransform.py b/src/pyGroupedTransforms/GroupedTransform.py index 2587a89..8453da4 100644 --- a/src/pyGroupedTransforms/GroupedTransform.py +++ b/src/pyGroupedTransforms/GroupedTransform.py @@ -1,12 +1,11 @@ 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.... @@ -272,20 +271,19 @@ 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 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 = [] @@ -294,8 +292,7 @@ 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) @@ -310,13 +307,9 @@ 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": @@ -325,8 +318,8 @@ 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 = ( @@ -350,7 +343,9 @@ 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: @@ -365,15 +360,15 @@ 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()) @@ -387,7 +382,9 @@ 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()) @@ -409,10 +406,15 @@ 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] @@ -492,15 +494,15 @@ 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( @@ -528,10 +530,10 @@ 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: diff --git a/src/pyGroupedTransforms/NFFTtools.py b/src/pyGroupedTransforms/NFFTtools.py index d0b02c6..432ac47 100644 --- a/src/pyGroupedTransforms/NFFTtools.py +++ b/src/pyGroupedTransforms/NFFTtools.py @@ -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, @@ -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}