Merge branch 'master' of gitlab.mpcdf.mpg.de:ift/NIFTy

demos/wiener_filter_hamiltonian.py

@@ -24,7 +24,7 @@ class WienerFilterEnergy(Energy):
     @property
     def value(self):
         D_inv_x = self.D_inverse_x()
-        H = 0.5 * D_inv_x.dot(self.position) - self.j.dot(self.position)
+        H = 0.5 * D_inv_x.vdot(self.position) - self.j.dot(self.position)
         return H.real
 
     @property

nifty/energies/line_energy.py

@@ -109,7 +109,7 @@ class LineEnergy(Energy):
 
     @property
     def gradient(self):
-        return self.energy.gradient.dot(self.line_direction)
+        return self.energy.gradient.vdot(self.line_direction)
 
     @property
     def curvature(self):

nifty/field.py

@@ -1040,7 +1040,7 @@ class Field(Loggable, Versionable, object):
         new_field.set_val(new_val=new_val, copy=False)
         return new_field
 
-    def dot(self, x=None, spaces=None, bare=False):
+    def vdot(self, x=None, spaces=None, bare=False):
         """ Computes the volume-factor-aware dot product of 'self' with x.
 
         Parameters

nifty/minimization/conjugate_gradient.py

@@ -121,12 +121,12 @@ class ConjugateGradient(Loggable, object):
 
         r = b - A(x0)
         d = self.preconditioner(r)
-        previous_gamma = r.dot(d)
+        previous_gamma = r.vdot(d)
         if previous_gamma == 0:
             self.logger.info("The starting guess is already perfect solution "
                              "for the inverse problem.")
             return x0, self.convergence_level+1
-        norm_b = np.sqrt(b.dot(b))
+        norm_b = np.sqrt(b.vdot(b))
         x = x0
         convergence = 0
         iteration_number = 1
@@ -137,7 +137,7 @@ class ConjugateGradient(Loggable, object):
                 self.callback(x, iteration_number)
 
             q = A(d)
-            alpha = previous_gamma/d.dot(q)
+            alpha = previous_gamma/d.vdot(q)
 
             if not np.isfinite(alpha):
                 self.logger.error("Alpha became infinite! Stopping.")
@@ -158,7 +158,7 @@ class ConjugateGradient(Loggable, object):
                 r -= q * alpha
 
             s = self.preconditioner(r)
-            gamma = r.dot(s)
+            gamma = r.vdot(s)
 
             if gamma.real < 0:
                 self.logger.warn("Positive definitness of preconditioner "

nifty/minimization/descent_minimizer.py

@@ -137,7 +137,7 @@ class DescentMinimizer(Loggable, object):
 
             # compute the the gradient for the current location
             gradient = energy.gradient
-            gradient_norm = gradient.dot(gradient)
+            gradient_norm = gradient.vdot(gradient)
 
             # check if position is at a flat point
             if gradient_norm == 0:

nifty/minimization/vl_bfgs.py

@@ -261,7 +261,7 @@ class InformationStore(object):
         """
         key = tuple(sorted((i, j)))
         if key not in self._ss_store:
-            self._ss_store[key] = self.s[i].dot(self.s[j])
+            self._ss_store[key] = self.s[i].vdot(self.s[j])
         return self._ss_store[key]
 
     def sy_store(self, i, j):
@@ -284,7 +284,7 @@ class InformationStore(object):
         """
         key = (i, j)
         if key not in self._sy_store:
-            self._sy_store[key] = self.s[i].dot(self.y[j])
+            self._sy_store[key] = self.s[i].vdot(self.y[j])
         return self._sy_store[key]
 
     def yy_store(self, i, j):
@@ -307,7 +307,7 @@ class InformationStore(object):
         """
         key = tuple(sorted((i, j)))
         if key not in self._yy_store:
-            self._yy_store[key] = self.y[i].dot(self.y[j])
+            self._yy_store[key] = self.y[i].vdot(self.y[j])
         return self._yy_store[key]
 
     def sgrad_store(self, i):
@@ -319,7 +319,7 @@ class InformationStore(object):
             Scalar product.
 
         """
-        return self.s[i].dot(self.last_gradient)
+        return self.s[i].vdot(self.last_gradient)
 
     def ygrad_store(self, i):
         """Returns scalar product between y_i and gradient on initial position.
@@ -330,7 +330,7 @@ class InformationStore(object):
             Scalar product.
 
         """
-        return self.y[i].dot(self.last_gradient)
+        return self.y[i].vdot(self.last_gradient)
 
     def gradgrad_store(self):
         """Returns scalar product of gradient on initial position with itself.
@@ -341,7 +341,7 @@ class InformationStore(object):
             Scalar product.
 
         """
-        return self.last_gradient.dot(self.last_gradient)
+        return self.last_gradient.vdot(self.last_gradient)
 
     def add_new_point(self, x, gradient):
         """Updates the s list and y list.

nifty/operators/composed_operator/composed_operator.py

@@ -51,7 +51,7 @@ class ComposedOperator(LinearOperator):
 
     Notes
     -----
-    Very usefull in case one has to transform a Field living over a product
+    Very useful in case one has to transform a Field living over a product
     space (see example below).
 
     Examples

nifty/probing/mixin_classes/trace_prober_mixin.py

@@ -30,7 +30,7 @@ class TraceProberMixin(object):
         super(TraceProberMixin, self).reset()
 
     def finish_probe(self, probe, pre_result):
-        result = probe[1].dot(pre_result, bare=True)
+        result = probe[1].vdot(pre_result, bare=True)
         self.__sum_of_probings += result
         if self.compute_variance:
             self.__sum_of_squares += result.conjugate() * result

test/test_operators/test_composed_operator.py

@@ -40,8 +40,8 @@ class ComposedOperator_Tests(unittest.TestCase):
         rand1 = Field.from_random('normal', domain=(space1,space2))
         rand2 = Field.from_random('normal', domain=(space1,space2))
 
-        tt1 = rand2.dot(op.times(rand1))
-        tt2 = rand1.dot(op.adjoint_times(rand2))
+        tt1 = rand2.vdot(op.times(rand1))
+        tt2 = rand1.vdot(op.adjoint_times(rand2))
         assert_approx_equal(tt1, tt2)
 
     @expand(product(spaces, spaces))

test/test_operators/test_diagonal_operator.py

@@ -33,8 +33,8 @@ class DiagonalOperator_Tests(unittest.TestCase):
         rand2 = Field.from_random('normal', domain=space)
         diag = Field.from_random('normal', domain=space)
         D = DiagonalOperator(space, diagonal=diag, bare=bare, copy=copy)
-        tt1 = rand1.dot(D.times(rand2))
-        tt2 = rand2.dot(D.times(rand1))
+        tt1 = rand1.vdot(D.times(rand2))
+        tt2 = rand2.vdot(D.times(rand1))
         assert_approx_equal(tt1, tt2)
 
     @expand(product(spaces, [True, False], [True, False]))

test/test_operators/test_fft_operator.py

@@ -143,8 +143,8 @@ class FFTOperatorTests(unittest.TestCase):
         inp = Field.from_random(domain=a, random_type='normal', std=1, mean=0,
                                 dtype=tp)
         out = fft.times(inp)
-        v1 = np.sqrt(out.dot(out))
-        v2 = np.sqrt(inp.dot(fft.adjoint_times(out)))
+        v1 = np.sqrt(out.vdot(out))
+        v2 = np.sqrt(inp.vdot(fft.adjoint_times(out)))
         assert_allclose(v1, v2, rtol=tol, atol=tol)
 
     @expand(product([128, 256],
@@ -159,6 +159,6 @@ class FFTOperatorTests(unittest.TestCase):
         inp = Field.from_random(domain=a, random_type='normal', std=1, mean=0,
                                 dtype=tp)
         out = fft.times(inp)
-        v1 = np.sqrt(out.dot(out))
-        v2 = np.sqrt(inp.dot(fft.adjoint_times(out)))
+        v1 = np.sqrt(out.vdot(out))
+        v2 = np.sqrt(inp.vdot(fft.adjoint_times(out)))
         assert_allclose(v1, v2, rtol=tol, atol=tol)

test/test_operators/test_response_operator.py

@@ -27,6 +27,6 @@ class ResponseOperator_Tests(unittest.TestCase):
                               exposure=[exposure])
         rand1 = Field.from_random('normal', domain=space)
         rand2 = Field.from_random('normal', domain=op.target[0])
-        tt1 = rand2.dot(op.times(rand1))
-        tt2 = rand1.dot(op.adjoint_times(rand2))
+        tt1 = rand2.vdot(op.times(rand1))
+        tt2 = rand1.vdot(op.adjoint_times(rand2))
         assert_approx_equal(tt1, tt2)

test/test_operators/test_smoothing_operator.py

@@ -60,8 +60,8 @@ class SmoothingOperator_Tests(unittest.TestCase):
                               log_distances=log_distances)
         rand1 = Field.from_random('normal', domain=space)
         rand2 = Field.from_random('normal', domain=space)
-        tt1 = rand1.dot(op.times(rand2))
-        tt2 = rand2.dot(op.adjoint_times(rand1))
+        tt1 = rand1.vdot(op.times(rand2))
+        tt2 = rand2.vdot(op.adjoint_times(rand1))
         assert_approx_equal(tt1, tt2)
 
     @expand(product(spaces, [0., .5, 5.], [False]))
@@ -79,8 +79,8 @@ class SmoothingOperator_Tests(unittest.TestCase):
                               log_distances=log_distances)
         rand1 = Field.from_random('normal', domain=space)
         rand2 = Field.from_random('normal', domain=space)
-        tt1 = rand1.dot(op.inverse_times(rand2))
-        tt2 = rand2.dot(op.inverse_adjoint_times(rand1))
+        tt1 = rand1.vdot(op.inverse_times(rand2))
+        tt2 = rand2.vdot(op.inverse_adjoint_times(rand1))
         assert_approx_equal(tt1, tt2)
 
     @expand(product([100, 200], [1, 0.4], [0., 1.,  3.7],