diff --git a/desc/compute/utils.py b/desc/compute/utils.py
index 89ad8f5c19..ec1fb823eb 100644
--- a/desc/compute/utils.py
+++ b/desc/compute/utils.py
@@ -332,14 +332,14 @@ def get_transforms(keys, obj, grid, jitable=False, **kwargs):
     derivs = get_derivs(keys, obj, has_axis=grid.axis.size)
     transforms = {"grid": grid}
     for c in derivs.keys():
-        if hasattr(obj, c + "_basis"):
+        if hasattr(obj, c + "_basis"):  # regular stuff like R, Z, lambda etc.
             basis = getattr(obj, c + "_basis")
             # first check if we already have a transform with a compatible basis
             for transform in transforms.values():
                 if basis.eq(getattr(transform, "basis", None)):
                     ders = np.unique(
                         np.vstack([derivs[c], transform.derivatives]), axis=0
-                    )
+                    ).astype(int)
                     # don't build until we know all the derivs we need
                     transform.change_derivatives(ders, build=False)
                     c_transform = transform
@@ -353,7 +353,7 @@ def get_transforms(keys, obj, grid, jitable=False, **kwargs):
                     method=method,
                 )
             transforms[c] = c_transform
-        elif c == "B":
+        elif c == "B":  # for fitting Boozer harmonics
             transforms["B"] = Transform(
                 grid,
                 DoubleFourierSeries(
@@ -367,7 +367,7 @@ def get_transforms(keys, obj, grid, jitable=False, **kwargs):
                 build_pinv=True,
                 method=method,
             )
-        elif c == "w":
+        elif c == "w":  # for fitting Boozer toroidal stream function
             transforms["w"] = Transform(
                 grid,
                 DoubleFourierSeries(
@@ -381,7 +381,7 @@ def get_transforms(keys, obj, grid, jitable=False, **kwargs):
                 build_pinv=True,
                 method=method,
             )
-        elif c not in transforms:
+        elif c not in transforms:  # possible other stuff lumped in with transforms
             transforms[c] = getattr(obj, c)
 
     # now build them
diff --git a/desc/transform.py b/desc/transform.py
index 7ff2e2bfb2..5ee180d0fb 100644
--- a/desc/transform.py
+++ b/desc/transform.py
@@ -82,7 +82,6 @@ def __init__(
         self._method = method
         # assign according to logic in setter function
         self.method = method
-        self._matrices = self._get_matrices()
         if build:
             self.build()
         if build_pinv:
@@ -374,6 +373,8 @@ def build(self):
             self._built = True
             return
 
+        self._matrices = self._get_matrices()
+
         if self.method == "direct1":
             for d in self.derivatives:
                 self.matrices["direct1"][d[0]][d[1]][d[2]] = self.basis.evaluate(
@@ -389,7 +390,7 @@ def build(self):
         if self.method in ["fft", "direct2"]:
             temp_d = np.hstack(
                 [self.derivatives[:, :2], np.zeros((len(self.derivatives), 1))]
-            )
+            ).astype(int)
             temp_modes = np.hstack([self.lm_modes, np.zeros((self.num_lm_modes, 1))])
             for d in temp_d:
                 self.matrices["fft"][d[0]][d[1]] = self.basis.evaluate(
@@ -398,7 +399,7 @@ def build(self):
         if self.method == "direct2":
             temp_d = np.hstack(
                 [np.zeros((len(self.derivatives), 2)), self.derivatives[:, 2:]]
-            )
+            ).astype(int)
             temp_modes = np.hstack(
                 [np.zeros((self.num_n_modes, 2)), self.n_modes[:, np.newaxis]]
             )