misc cleanup, reducing pr delta from draft work

Author: garrettwrong

src/aspire/image/xform.py

@@ -144,7 +144,6 @@ def __init__(self, factor):
         self.multipliers = np.array(factor)
 
     def _forward(self, im, indices):
-
         if self.multipliers.size == 1:  # if we have a scalar multiplier
             im_new = im * self.multipliers.astype(im.dtype)
         else:

src/aspire/reconstruction/estimator.py

@@ -159,6 +159,7 @@ def apply_kernel(self, vol_coef, kernel=None):
 
         if kernel is None:
             kernel = self.kernel
+
         vol = Coef(self.basis, vol_coef).evaluate()  # returns a Volume
         vol = kernel.convolve_volume(vol)  # returns a Volume
         vol_coef = self.basis.evaluate_t(vol)

src/aspire/reconstruction/mean.py

@@ -5,9 +5,10 @@
 from scipy.linalg import norm
 from scipy.sparse.linalg import LinearOperator
 
+from aspire import config
 from aspire.basis import Coef
 from aspire.nufft import anufft
-from aspire.numeric import config, fft
+from aspire.numeric import fft
 from aspire.numeric.scipy import cg
 from aspire.operators import evaluate_src_filters_on_grid
 from aspire.reconstruction import Estimator, FourierKernel, FourierKernelMatrix
@@ -211,7 +212,6 @@ def conj_grad(self, b_coef, x0=None, tol=1e-5, regularizer=0):
             precond_kernel = self.precond_kernel
             if regularizer > 0:
                 precond_kernel += regularizer
-
             M = LinearOperator(
                 (self.r * count, self.r * count),
                 matvec=partial(self.apply_kernel, kernel=precond_kernel),