PEP8 fixes

cookbook/seismic_srtomo_sharp.py

@@ -27,8 +27,8 @@
 # Make the mesh
 mesh = SquareMesh(area, shape)
 # and run the inversion
-tomo = srtomo.SRTomo(tts, srcs, recs, mesh) \
-       + 30*TotalVariation2D(1e-10, mesh.shape)
+tomo = (srtomo.SRTomo(tts, srcs, recs, mesh) +
+        30*TotalVariation2D(1e-10, mesh.shape))
 # Since Total Variation is a non-linear function, then the tomography becomes
 # non-linear. So we need to configure fit to use the Levemberg-Marquardt
 # algorithm, a gradient descent method, that requires an initial estimate

fatiando/__init__.py

@@ -30,8 +30,8 @@
 implementing inverse problems. There you'll find ready to use regularization,
 optimization methods, and templates to implement new inversion methods.
 
-Inversions implemented in Fatiando leverage :mod:`fatiando.inversion`, providing
-a common interface and usage patters. For examples, see modules
+Inversions implemented in Fatiando leverage :mod:`fatiando.inversion`,
+providing a common interface and usage patters. For examples, see modules
 :mod:`fatiando.seismic.epic2d`,
 :mod:`fatiando.seismic.srtomo`,
 :mod:`fatiando.gravmag.basin2d`,

fatiando/gravmag/basin2d.py

@@ -473,18 +473,12 @@ def jacobian(self, p):
         verts = self.verts
         delta = 1.
         jac = np.empty((self.ndata, self.nparams), dtype=np.float)
-        jac[:, 0] = (
-            talwani.gz(x, z, [Polygon(verts + [[x1, z1 + delta], [x2, z2]],
-                                      props)])
-             - talwani.gz(x, z, [Polygon(verts + [[x1, z1 - delta], [x2, z2]],
-                                         props)])
-             )/(2*delta)
-        jac[:, 1] = (
-            talwani.gz(x, z, [Polygon(verts + [[x1, z1], [x2, z2 + delta]],
-                                      props)])
-             - talwani.gz(x, z, [Polygon(verts + [[x1, z1], [x2, z2 - delta]],
-                                         props)])
-             )/(2*delta)
+        z1p = [Polygon(verts + [[x1, z1 + delta], [x2, z2]], props)]
+        z1m = [Polygon(verts + [[x1, z1 - delta], [x2, z2]], props)]
+        jac[:, 0] = (talwani.gz(x, z, z1p) - talwani.gz(x, z, z1m))/(2*delta)
+        z2p = [Polygon(verts + [[x1, z1], [x2, z2 + delta]], props)]
+        z2m = [Polygon(verts + [[x1, z1], [x2, z2 - delta]], props)]
+        jac[:, 1] = (talwani.gz(x, z, z2p) - talwani.gz(x, z, z2m))/(2*delta)
         return jac
 
     def fmt_estimate(self, p):

fatiando/gravmag/euler.py

@@ -83,7 +83,7 @@ def __init__(self, x, y, z, field, xderiv, yderiv, zderiv,
             "Invalid structural index '{}'. Should be >= 0".format(
                 structural_index)
         super().__init__(
-            data=-x*xderiv - y*yderiv -z*zderiv - structural_index*field,
+            data=-x*xderiv - y*yderiv - z*zderiv - structural_index*field,
             nparams=4, islinear=True)
         self.x = x
         self.y = y

fatiando/seismic/epic2d.py

@@ -110,16 +110,16 @@ def predicted(self, p):
         "Calculate the predicted travel time data given a parameter vector."
         x, y = p
         alpha = 1/self.vs - 1/self.vp
-        pred = alpha*np.sqrt((self.recs[:, 0] - x)**2
-                              + (self.recs[:, 1] - y)**2)
+        pred = alpha*np.sqrt((self.recs[:, 0] - x)**2 +
+                             (self.recs[:, 1] - y)**2)
         return pred
 
     def jacobian(self, p):
         "Calculate the Jacobian matrix for the inversion."
         x, y = p
         alpha = 1/self.vs - 1/self.vp
-        sqrt = np.sqrt((self.recs[:, 0] - x)**2
-                        + (self.recs[:, 1] - y)**2)
+        sqrt = np.sqrt((self.recs[:, 0] - x)**2 +
+                       (self.recs[:, 1] - y)**2)
         jac = np.empty((self.ndata, self.nparams))
         jac[:, 0] = -alpha*(self.recs[:, 0] - x)/sqrt
         jac[:, 1] = -alpha*(self.recs[:, 1] - y)/sqrt