diff --git a/pyproject.toml b/pyproject.toml
index f02cdf2..2596536 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "pyGroupedTransforms"
-version = "1.0.0"
+version = "1.1.0"
 authors = [
   { name="Felix Wirth", email="fwi012001@gmail.com" },
 ]
diff --git a/src/pyGroupedTransforms/NFCTtools.py b/src/pyGroupedTransforms/NFCTtools.py
index c85628a..99efef7 100644
--- a/src/pyGroupedTransforms/NFCTtools.py
+++ b/src/pyGroupedTransforms/NFCTtools.py
@@ -77,8 +77,7 @@ def nfct_index_set(
     if d == 1:
         return np.array([i for i in range(0, bandwidths[0])], "int")
 
-    bandwidths = bandwidths[::-1]
-    tmp = tuple([list(range(0, bw)) for bw in bandwidths])
+    tmp = tuple([list(range(0, bw)) for bw in bandwidths[::-1]])
     tmp = itertools.product(*(tmp[::-1]))
 
     freq = np.empty((d, np.prod(bandwidths)), dtype=int)
@@ -127,7 +126,7 @@ 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(
diff --git a/src/pyGroupedTransforms/NFFTtools.py b/src/pyGroupedTransforms/NFFTtools.py
index 43f4f3c..2237e87 100644
--- a/src/pyGroupedTransforms/NFFTtools.py
+++ b/src/pyGroupedTransforms/NFFTtools.py
@@ -88,7 +88,7 @@ def nfft_index_set(
             list(range(int(-bandwidths[0] / 2), int(bandwidths[0] / 2))), dtype="int"
         )
 
-    tmp = [list(range(int(-bw / 2), int(bw / 2))) for bw in bandwidths]
+    tmp = [list(range(int(-bw / 2), int(bw / 2))) for bw in bandwidths[::-1]]
     tmp = itertools.product(*(tmp[::-1]))
 
     freq = np.empty((d, np.prod(bandwidths)), dtype=int)
@@ -134,11 +134,10 @@ def get_transform(
     # Output:
      * `F::LinearMap{ComplexF64}` ... Linear map of the Fourier-transform implemented by the NFFT
     """
-
     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(