Auto code format

Author: passscoed

src/pyGroupedTransforms/GroupedTransform.py

@@ -435,7 +435,9 @@ def get_matrix(self):
                 u1 = (0,)
             else:
                 u1 = s1.u
-            F_direct = s1.mode.get_matrix(s1.bandwidths, self.X[:, u1].T, bases=s1.bases)
+            F_direct = s1.mode.get_matrix(
+                s1.bandwidths, self.X[:, u1].T, bases=s1.bases
+            )
             for idx, s in enumerate(self.settings):
                 if idx == 0:
                     continue

src/pyGroupedTransforms/NFCTtools.py

@@ -127,7 +127,7 @@ def get_transform(
     if bandwidths.ndim > 1 or bandwidths.dtype != "int32":
         return "Please use an zero or one-dimensional numpy.array with dtype 'int32' as input"
 
-    (M, d) = X.shape
+    M, d = X.shape
 
     if len(bandwidths) == 0:
         return DeferredLinearOperator(

src/pyGroupedTransforms/NFFTtools.py

@@ -138,7 +138,7 @@ def get_transform(
     if bandwidths.ndim > 1 or bandwidths.dtype != "int32":
         return "Please use an zero or one-dimensional numpy.array with dtype 'int32' as input"
 
-    (M, d) = np.shape(X)
+    M, d = np.shape(X)
 
     if len(bandwidths) == 0:
         return DeferredLinearOperator(

src/pyGroupedTransforms/NFMTtools.py

@@ -1,126 +1,126 @@
 import numpy as np
+from pyNFFT3.NFMT import BASES
 
 from pyGroupedTransforms import *
-from pyNFFT3.NFMT import BASES
 
 
 def datalength(bandwidths: np.ndarray) -> int:
-	"""Return the number of mixed-basis Fourier coefficients (zero-frequencies excluded).
+    """Return the number of mixed-basis Fourier coefficients (zero-frequencies excluded).
 
-	For each dimension j the frequency range used is:
-	  exp  -> {-N_j/2, ..., -1, 1, ..., N_j/2 - 1}  (N_j - 1 values)
-	  cos/alg -> {1, 2, ..., N_j - 1}                 (N_j - 1 values)
-	Total: prod(N - 1).
-	"""
-	if bandwidths.ndim != 1 or bandwidths.dtype != "int32":
-		return "Please use an one-dimensional numpy.array with dtype 'int32' as input"
-	if len(bandwidths) == 0:
-		return 1
-	return int(np.prod(bandwidths - 1))
+    For each dimension j the frequency range used is:
+      exp  -> {-N_j/2, ..., -1, 1, ..., N_j/2 - 1}  (N_j - 1 values)
+      cos/alg -> {1, 2, ..., N_j - 1}                 (N_j - 1 values)
+    Total: prod(N - 1).
+    """
+    if bandwidths.ndim != 1 or bandwidths.dtype != "int32":
+        return "Please use an one-dimensional numpy.array with dtype 'int32' as input"
+    if len(bandwidths) == 0:
+        return 1
+    return int(np.prod(bandwidths - 1))
 
 
 def nfmt_index_set_without_zeros(bandwidths: np.ndarray, bases) -> np.ndarray:
-	"""Return the (d, prod(N-1)) integer frequency matrix.
-
-	Mirrors Julia's GroupedTransforms.NFMTtools.nfmt_index_set_without_zeros:
-	  exp  dimension j: {-N_j/2, ..., -1, 1, ..., N_j/2 - 1}
-	  cos/alg dimension j: {1, 2, ..., N_j - 1}
-	"""
-	d = len(bandwidths)
-	if d == 0:
-		return np.array([[0]], dtype=np.int64)
-
-	ranges = []
-	for n, basis in zip(bandwidths, bases):
-		if BASES[basis] > 0:
-			# cos or alg: {1, ..., N-1}
-			ranges.append(list(range(1, int(n))))
-		else:
-			# exp: {-N/2, ..., -1, 1, ..., N/2-1}
-			half = int(n) // 2
-			ranges.append(list(range(-half, 0)) + list(range(1, half)))
-
-	if d == 1:
-		return np.array(ranges[0], dtype=np.int64).reshape(1, -1)
-
-	# Cartesian product; last dimension changes fastest (row-major, matching Julia)
-	mesh = np.array(np.meshgrid(*ranges, indexing="ij"), dtype=np.int64)
-	return mesh.reshape(d, -1)
+    """Return the (d, prod(N-1)) integer frequency matrix.
+
+    Mirrors Julia's GroupedTransforms.NFMTtools.nfmt_index_set_without_zeros:
+      exp  dimension j: {-N_j/2, ..., -1, 1, ..., N_j/2 - 1}
+      cos/alg dimension j: {1, 2, ..., N_j - 1}
+    """
+    d = len(bandwidths)
+    if d == 0:
+        return np.array([[0]], dtype=np.int64)
+
+    ranges = []
+    for n, basis in zip(bandwidths, bases):
+        if BASES[basis] > 0:
+            # cos or alg: {1, ..., N-1}
+            ranges.append(list(range(1, int(n))))
+        else:
+            # exp: {-N/2, ..., -1, 1, ..., N/2-1}
+            half = int(n) // 2
+            ranges.append(list(range(-half, 0)) + list(range(1, half)))
+
+    if d == 1:
+        return np.array(ranges[0], dtype=np.int64).reshape(1, -1)
+
+    # Cartesian product; last dimension changes fastest (row-major, matching Julia)
+    mesh = np.array(np.meshgrid(*ranges, indexing="ij"), dtype=np.int64)
+    return mesh.reshape(d, -1)
 
 
 def get_matrix(bandwidths, X, bases) -> np.ndarray:
-	"""Build the evaluation matrix F with F[m, j] = phi(x_m, k_j).
-
-	Mirrors Julia's GroupedTransforms.NFMTtools.get_matrix / get_phi:
-	  exp:     exp(-2pi i k x)
-	  cos:     sqrt(2) * cos(pi * k * x)   (k != 0 guaranteed by frequency set)
-	  alg:     sqrt(2) * cos(k * arccos(2x - 1))
-
-	X is expected in (d, M) format (columns are nodes), consistent with the
-	convention used in GroupedTransform.get_matrix.
-	"""
-	if X.ndim == 1 or X.shape[0] == 1:
-		X_eval = X.flatten().reshape(-1, 1)   # (M, 1)
-		d = 1
-		M = X_eval.shape[0]
-	else:
-		d, M = X.shape
-		X_eval = X.T                           # (M, d)
-
-	if len(bandwidths) == 0:
-		return np.ones((M, 1), dtype=np.complex128)
-
-	freq = nfmt_index_set_without_zeros(
-		np.asarray(bandwidths, dtype=np.int32), list(bases)
-	)                                          # (d, nf)
-
-	nf = freq.shape[1]
-	F = np.ones((M, nf), dtype=np.complex128)
-
-	for j in range(d):
-		n_j = freq[j]                          # (nf,)  all nonzero by construction
-		x_j = X_eval[:, j][:, None]           # (M, 1)
-		if BASES[bases[j]] == 1:
-			# cos basis: sqrt(2) * cos(pi * k * x)
-			F *= np.sqrt(2.0) * np.cos(np.pi * x_j * n_j)
-		elif BASES[bases[j]] == 2:
-			# alg (Chebyshev) basis: sqrt(2) * cos(k * arccos(2x - 1))
-			F *= np.sqrt(2.0) * np.cos(n_j * np.arccos(2.0 * x_j - 1.0))
-		else:
-			# exp basis: exp(-2pi i k x)
-			F *= np.exp(-2.0j * np.pi * x_j * n_j)
-
-	return F
+    """Build the evaluation matrix F with F[m, j] = phi(x_m, k_j).
+
+    Mirrors Julia's GroupedTransforms.NFMTtools.get_matrix / get_phi:
+      exp:     exp(-2pi i k x)
+      cos:     sqrt(2) * cos(pi * k * x)   (k != 0 guaranteed by frequency set)
+      alg:     sqrt(2) * cos(k * arccos(2x - 1))
+
+    X is expected in (d, M) format (columns are nodes), consistent with the
+    convention used in GroupedTransform.get_matrix.
+    """
+    if X.ndim == 1 or X.shape[0] == 1:
+        X_eval = X.flatten().reshape(-1, 1)  # (M, 1)
+        d = 1
+        M = X_eval.shape[0]
+    else:
+        d, M = X.shape
+        X_eval = X.T  # (M, d)
+
+    if len(bandwidths) == 0:
+        return np.ones((M, 1), dtype=np.complex128)
+
+    freq = nfmt_index_set_without_zeros(
+        np.asarray(bandwidths, dtype=np.int32), list(bases)
+    )  # (d, nf)
+
+    nf = freq.shape[1]
+    F = np.ones((M, nf), dtype=np.complex128)
+
+    for j in range(d):
+        n_j = freq[j]  # (nf,)  all nonzero by construction
+        x_j = X_eval[:, j][:, None]  # (M, 1)
+        if BASES[bases[j]] == 1:
+            # cos basis: sqrt(2) * cos(pi * k * x)
+            F *= np.sqrt(2.0) * np.cos(np.pi * x_j * n_j)
+        elif BASES[bases[j]] == 2:
+            # alg (Chebyshev) basis: sqrt(2) * cos(k * arccos(2x - 1))
+            F *= np.sqrt(2.0) * np.cos(n_j * np.arccos(2.0 * x_j - 1.0))
+        else:
+            # exp basis: exp(-2pi i k x)
+            F *= np.exp(-2.0j * np.pi * x_j * n_j)
+
+    return F
 
 
 def get_transform(bandwidths: np.ndarray, X: np.ndarray, bases):
-	"""Return a DeferredLinearOperator for the mixed-basis grouped transform.
-
-	Uses the same matrix as get_matrix wrapped in a linear operator so that
-	trafo and adjoint are consistent by construction.
-	X is expected in (M, d) format (rows are nodes), consistent with the
-	convention used in GroupedTransform.transforms.
-	"""
-	if bandwidths.ndim > 1 or bandwidths.dtype != "int32":
-		return "Please use a zero or one-dimensional numpy.array with dtype 'int32' as input"
-
-	M = X.shape[0]
-
-	if len(bandwidths) == 0:
-		return DeferredLinearOperator(
-			dtype=np.complex128,
-			shape=(M, 1),
-			mfunc=lambda fhat: np.full(M, fhat[0], dtype=np.complex128),
-			rmfunc=lambda f: np.array([np.sum(f)], dtype=np.complex128),
-		)
-
-	# X is (M, d) here; get_matrix expects (d, M)
-	mat = get_matrix(bandwidths, X.T, list(bases))   # (M, nf)
-	N = int(np.prod(bandwidths - 1))
-
-	return DeferredLinearOperator(
-		dtype=np.complex128,
-		shape=(M, N),
-		mfunc=lambda fhat: mat @ np.asarray(fhat, dtype=np.complex128),
-		rmfunc=lambda f: mat.conj().T @ np.asarray(f, dtype=np.complex128),
-	)
+    """Return a DeferredLinearOperator for the mixed-basis grouped transform.
+
+    Uses the same matrix as get_matrix wrapped in a linear operator so that
+    trafo and adjoint are consistent by construction.
+    X is expected in (M, d) format (rows are nodes), consistent with the
+    convention used in GroupedTransform.transforms.
+    """
+    if bandwidths.ndim > 1 or bandwidths.dtype != "int32":
+        return "Please use a zero or one-dimensional numpy.array with dtype 'int32' as input"
+
+    M = X.shape[0]
+
+    if len(bandwidths) == 0:
+        return DeferredLinearOperator(
+            dtype=np.complex128,
+            shape=(M, 1),
+            mfunc=lambda fhat: np.full(M, fhat[0], dtype=np.complex128),
+            rmfunc=lambda f: np.array([np.sum(f)], dtype=np.complex128),
+        )
+
+    # X is (M, d) here; get_matrix expects (d, M)
+    mat = get_matrix(bandwidths, X.T, list(bases))  # (M, nf)
+    N = int(np.prod(bandwidths - 1))
+
+    return DeferredLinearOperator(
+        dtype=np.complex128,
+        shape=(M, N),
+        mfunc=lambda fhat: mat @ np.asarray(fhat, dtype=np.complex128),
+        rmfunc=lambda f: mat.conj().T @ np.asarray(f, dtype=np.complex128),
+    )

tests/nfmt_U.py

@@ -24,9 +24,7 @@
 
 # set up transform ###################################################
 
-F = GroupedTransform(
-    "mixed", X, U=U, N=[0, 64, 16], basis_vect=basis_vect
-)
+F = GroupedTransform("mixed", X, U=U, N=[0, 64, 16], basis_vect=basis_vect)
 F_direct = F.get_matrix()
 
 # compute transform with NFMT ########################################

tests/nfmt_ds.py

@@ -22,9 +22,7 @@
 
 # set up transform ###################################################
 
-F = GroupedTransform(
-    "mixed", X, d=d, ds=ds, N=[64, 16, 4], basis_vect=basis_vect
-)
+F = GroupedTransform("mixed", X, d=d, ds=ds, N=[64, 16, 4], basis_vect=basis_vect)
 F_direct = F.get_matrix()
 
 # compute transform with NFMT ########################################