Minor ctf tweaks

gallery/tutorials/ctf.py

@@ -62,6 +62,8 @@
 # For the general ``CTFFilter``,
 # we provide defocus along two perpendicular axes u and v separately,
 # along with the angle the u-axis makes with the horizontal (x) axis.
+# Note that we chose an extreme astigmatism for demonstration,
+# and the values more typically differ by a few percent.
 
 ctf_filter = CTFFilter(
     pixel_size=1,  # Angstrom

src/aspire/ctf/ctf_estimator.py

@@ -291,7 +291,7 @@ def background_subtract_1d(
         Estimate and subtract the background from the power spectrum
 
         :param amplitude_spectrum: Estimated power spectrum
-        :param linprog_method: Method passed to linear progam solver (scipy.optimize.linprog).  Defaults to 'interior-point'.
+        :param linprog_method: Method passed to linear program solver (scipy.optimize.linprog).
         :param n_low_freq_cutoffs: Low frequency cutoffs (loop iterations).
         :return: 2-tuple of NumPy arrays (PSD after noise subtraction and estimated noise)
         """
@@ -358,20 +358,22 @@ def background_subtract_1d(
                 axis=0,
             )
 
-            x_bound_lst = [
-                (signal[i], signal[i], -1 * np.inf, np.inf)
-                for i in range(signal.shape[0])
-            ]
-            x_bound = np.asarray(x_bound_lst, A.dtype)
-            x_bound = np.concatenate((x_bound[:, :2], x_bound[:, 2:]), axis=0)
+            # The original code used `bounds`,
+            #   but for many problems, linprog reports infeasable constraints.
+            # In practice for a micrograph from the paper, and our tutorial,
+            #   the code seems to work better without it...
+            # ASPIRE #417
 
             x = linprog(
                 f,
                 A_ub=A,
                 b_ub=np.zeros(A.shape[0]),
-                bounds=x_bound,
                 method=linprog_method,
             )
+
+            if not x.success:
+                raise RuntimeError("Linear program did not succeed. Halting")
+
             background = x.x[N:]
 
             bs_psd = signal - background

tests/test_CtfEstimate.py

@@ -74,8 +74,8 @@ def testEstimateCTF(self):
                     # defocusV
                     self.assertTrue(
                         np.allclose(
-                            result["defocus_u"],
-                            self.test_output["defocus_u"],
+                            result["defocus_v"],
+                            self.test_output["defocus_v"],
                             rtol=0.05,
                         )
                     )