STY: Fix many pylint warnings, add good names and remove local disables

Author: kohr-h

.pylintrc

@@ -47,16 +47,19 @@ disable=redefined-builtin,
   no-member,
   missing-super-argument,
   abstract-method,
-  protected-access
+  protected-access,
+  not-callable
 
-# redefined-builtin: 'object', 'range', 'str', 'super' are redefined, 
+# redefined-builtin: 'object', 'range', 'str', 'super' are redefined,
 #                    that's okay
 # no-name-in-module, no-member: triggered by scipy and numpy, let's ignore them
 # missing-super-argument: importing new super, pylint doesn't get it
 # abstract-method: some classes are intended as intermediate, where it's okay not to override
 #                  abstract methods. This warning does not really prevent bugs, so we're disabling it.
 # protected-access: We use private attributes of "internalized" objects. Since this check is not really
 #                   a bug catcher, we're disabling it.
+# not-callable: pylint tries to determine if a class attribute (e.g. element_type) is
+#               callable or not - and fails to do so
 
 [REPORTS]
 
@@ -131,10 +134,10 @@ indent-after-paren=4
 bad-functions=map,filter,apply,input
 
 # Good variable names which should always be accepted, separated by a comma
-good-names=i,j,k,ex,Run,_,a,b,x,y,z,xp,yp,zp,s,q,p,I,T,dx,dy,x1,x2,xi,yi,n,m,ax
+good-names=a,b,c,d,f,g,h,i,j,k,m,n,p,q,r,s,t,u,v,w,x,y,z,xp,yp,zp,dx,dy,dz,x0,x1,x2,y0,y1,y2,z0,z1,z2,xi,yi,zi,ax,rn,cn,op,ex,_,I,T,nx,ny,nz,fx
 
 # Bad variable names which should always be refused, separated by a comma
-bad-names=foo,bar,baz,toto,tutu,tata
+bad-names=foo,baz,toto,tutu,tata,l
 
 # Colon-delimited sets of names that determine each other's naming style when
 # the name regexes allow several styles.

examples/convolution_test.py

@@ -59,8 +59,8 @@ def opnorm(self):
 cont_space = odl.FunctionSpace(odl.Interval(0, 10))
 
 # Complicated functions to check performance
-cont_kernel = cont_space.element(lambda x: np.exp(x/2) * np.cos(x*1.172))
-cont_phantom = cont_space.element(lambda x: x**2 * np.sin(x)**2*(x > 5))
+cont_kernel = cont_space.element(lambda x: np.exp(x / 2) * np.cos(x * 1.172))
+cont_phantom = cont_space.element(lambda x: x ** 2 * np.sin(x) ** 2 * (x > 5))
 
 # Discretization
 discr_space = odl.uniform_discr_fromspace(cont_space, 500, impl='numpy')
@@ -72,7 +72,7 @@ def opnorm(self):
 
 # Dampening parameter for landweber
 iterations = 100
-omega = 1/conv.opnorm()**2
+omega = 1 / conv.opnorm() ** 2
 
 # Display partial
 partial = solvers.util.ForEachPartial(lambda result: plt.plot(conv(result)))

examples/convolution_test_cuda.py

@@ -64,8 +64,8 @@ def opnorm(self):  # An upper limit estimate of the operator norm
 cont_space = odl.FunctionSpace(odl.Interval(0, 10))
 
 # Complicated functions to check performance
-cont_kernel = cont_space.element(lambda x: np.exp(x/2) * np.cos(x*1.172))
-cont_data = cont_space.element(lambda x: x**2 * np.sin(x)**2*(x > 5))
+cont_kernel = cont_space.element(lambda x: np.exp(x / 2) * np.cos(x * 1.172))
+cont_data = cont_space.element(lambda x: x ** 2 * np.sin(x) ** 2 * (x > 5))
 
 # Discretization
 discr_space = odl.uniform_discr_fromspace(cont_space, 5000, impl='cuda')
@@ -77,7 +77,7 @@ def opnorm(self):  # An upper limit estimate of the operator norm
 
 # Dampening parameter for landweber
 iterations = 100
-omega = 1.0/conv.opnorm()**2
+omega = 1.0 / conv.opnorm() ** 2
 
 # Display partial
 partial = solvers.util.ForEachPartial(

examples/cudaspace_speedtest.py

@@ -78,5 +78,5 @@ def run_test(function, message):
 run_test(lambda z, x, y: x.space.lincomb(2, z, 2, z, z), "z = (a+b)*z")
 
 # Non lincomb tests
-run_test(lambda z, x, y: x+y, "z = x + y")
+run_test(lambda z, x, y: x + y, "z = x + y")
 run_test(lambda z, x, y: y.assign(x), "y.assign(x)")

examples/operator_test.py

@@ -33,6 +33,7 @@
 
 
 class Convolution(odl.Operator):
+
     def __init__(self, space, kernel, adjkernel):
         self.kernel = kernel
         self.adjkernel = adjkernel
@@ -58,7 +59,7 @@ def adjoint(self):
 def kernel(x):
     mean = [0.0, 0.25]
     std = [0.05, 0.05]
-    return np.exp(-(((x[0]-mean[0])/std[0])**2 + ((x[1]-mean[1])/std[1])**2))
+    return np.exp(-(((x[0] - mean[0]) / std[0])**2 + ((x[1] - mean[1]) / std[1])**2))
 
 
 def adjkernel(x):
@@ -71,8 +72,8 @@ def adjkernel(x):
 
 # Discretization parameters
 n = 20
-npoints = np.array([n+1, n+1])
-npoints_kernel = np.array([2*n+1, 2*n+1])
+npoints = np.array([n + 1, n + 1])
+npoints_kernel = np.array([2 * n + 1, 2 * n + 1])
 
 # Discretized spaces
 disc_space = odl.uniform_discr_fromspace(cont_space, npoints)

examples/reals.py

@@ -36,7 +36,7 @@ def _inner(self, x1, x2):
         return x1.__val__ * x2.__val__
 
     def _lincomb(self, a, x1, b, x2, out):
-        out.__val__ = a*x1.__val__ + b*x2.__val__
+        out.__val__ = a * x1.__val__ + b * x2.__val__
 
     def _multiply(self, x1, x2, out):
         out.__val__ = x1.__val__ * x2.__val__
@@ -70,7 +70,7 @@ def __str__(self):
 
     print(x)
     print(y)
-    print(x+y)
-    print(x*y)
-    print(x-y)
+    print(x + y)
+    print(x * y)
+    print(x - y)
     print(3.14 * x)

examples/solver_examples.py

@@ -27,7 +27,7 @@
 def landweber_base(operator, x, rhs, iterations=1, omega=1):
     """Straightforward implementation of Landweber's iteration."""
     for _ in range(iterations):
-        x = x - omega * operator.adjoint(operator(x)-rhs)
+        x = x - omega * operator.adjoint(operator(x) - rhs)
 
     return x
 
@@ -42,8 +42,8 @@ def conjugate_gradient_base(op, x, rhs, iterations=1):
         q = op(p)
         norms2 = s.norm()**2
         a = norms2 / q.norm()**2
-        x = x + a*p
-        d = d - a*q
+        x = x + a * p
+        d = d - a * q
         s = op.adjoint(d)
-        b = s.norm()**2/norms2
-        p = s + b*p
+        b = s.norm()**2 / norms2
+        p = s + b * p

examples/visualization/show_productspace.py

@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU General Public License
 # along with ODL.  If not, see <http://www.gnu.org/licenses/>.
 
-""" Example on using show with ProductSpace's """
+"""Example on using show with ProductSpace's."""
 
 # Imports for common Python 2/3 codebase
 from __future__ import print_function, division, absolute_import
@@ -42,5 +42,5 @@
          title='Show every third element')
 
 # Slices propagate (as in numpy)
-vec.show(indices=np.s_[2, :, n//2], show=True,
+vec.show(indices=np.s_[2, :, n // 2], show=True,
          title='Show second element, then slice by [:, n//2]')

examples/visualization/show_update_1d.py

@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU General Public License
 # along with ODL.  If not, see <http://www.gnu.org/licenses/>.
 
-""" Example on using show and updating the figure in real time in 1d. """
+"""Example on using show and updating the figure in real time in 1d."""
 
 # Imports for common Python 2/3 codebase
 from __future__ import print_function, division, absolute_import

examples/visualization/show_update_2d.py

@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU General Public License
 # along with ODL.  If not, see <http://www.gnu.org/licenses/>.
 
-""" Example on using show and updating the figure in real time in 2d. """
+"""Example on using show and updating the figure in real time in 2d."""
 
 # Imports for common Python 2/3 codebase
 from __future__ import print_function, division, absolute_import