diff --git a/nifty5/domains/rg_space.py b/nifty5/domains/rg_space.py
index d3db6a98f08910c18ef5351f2a0637805887cb01..181bc4359e3070ae4d2120eb2d8ff393ca03b353 100644
--- a/nifty5/domains/rg_space.py
+++ b/nifty5/domains/rg_space.py
@@ -141,9 +141,7 @@ class RGSpace(StructuredDomain):
@staticmethod
def _kernel(x, sigma):
from ..sugar import exp
- tmp = x*x
- tmp *= -2.*np.pi*np.pi*sigma*sigma
- return exp(tmp)
+ return exp(x*x * (-2.*np.pi*np.pi*sigma*sigma))
def get_fft_smoothing_kernel_function(self, sigma):
if (not self.harmonic):
diff --git a/nifty5/extra/energy_and_model_tests.py b/nifty5/extra/energy_and_model_tests.py
index 8f49f1499bce9ebf1af7700111c0e9ce69e09184..fbd1e6132b61123582386c559d88c9017d960d92 100644
--- a/nifty5/extra/energy_and_model_tests.py
+++ b/nifty5/extra/energy_and_model_tests.py
@@ -31,7 +31,7 @@ def _get_acceptable_model(M):
raise ValueError('Initial Model value must be finite')
dir = from_random("normal", M.position.domain)
dirder = M.jacobian(dir)
- dir *= val/(dirder).norm()*1e-5
+ dir = dir * val * (1e-5/dirder.norm())
# Find a step length that leads to a "reasonable" Model
for i in range(50):
try:
@@ -40,7 +40,7 @@ def _get_acceptable_model(M):
break
except FloatingPointError:
pass
- dir *= 0.5
+ dir = dir*0.5
else:
raise ValueError("could not find a reasonable initial step")
return M2
@@ -52,7 +52,7 @@ def _get_acceptable_energy(E):
raise ValueError('Initial Energy must be finite')
dir = from_random("normal", E.position.domain)
dirder = E.gradient.vdot(dir)
- dir *= np.abs(val)/np.abs(dirder)*1e-5
+ dir = dir * (np.abs(val)/np.abs(dirder)*1e-5)
# Find a step length that leads to a "reasonable" energy
for i in range(50):
try:
@@ -61,7 +61,7 @@ def _get_acceptable_energy(E):
break
except FloatingPointError:
pass
- dir *= 0.5
+ dir = dir*0.5
else:
raise ValueError("could not find a reasonable initial step")
return E2
@@ -92,7 +92,7 @@ def check_value_gradient_consistency(E, tol=1e-8, ntries=100):
xtol = tol*Emid.gradient_norm
if abs(numgrad-dirder) < xtol:
break
- dir *= 0.5
+ dir = dir*0.5
dirnorm *= 0.5
E2 = Emid
else:
@@ -117,7 +117,7 @@ def check_value_gradient_metric_consistency(E, tol=1e-8, ntries=100):
if abs((E2.value-val)/dirnorm - dirder) < xtol and \
(abs((E2.gradient-E.gradient)/dirnorm-dgrad) < xtol).all():
break
- dir *= 0.5
+ dir = dir*0.5
dirnorm *= 0.5
E2 = Emid
else:
diff --git a/nifty5/field.py b/nifty5/field.py
index 8b5b2344b396e0f4ad4cd38e5b53a8022625da44..4e85babf7b373acf1d79759ce5976ea292aff530 100644
--- a/nifty5/field.py
+++ b/nifty5/field.py
@@ -670,3 +670,12 @@ for op in ["__add__", "__radd__",
return NotImplemented
return func2
setattr(Field, op, func(op))
+
+for op in ["__iadd__", "__isub__", "__imul__", "__idiv__",
+ "__itruediv__", "__ifloordiv__", "__ipow__"]:
+ def func(op):
+ def func2(self, other):
+ raise TypeError(
+ "In-place operations are deliberately not supported")
+ return func2
+ setattr(Field, op, func(op))
diff --git a/nifty5/minimization/conjugate_gradient.py b/nifty5/minimization/conjugate_gradient.py
index 94cd8991e9f74bf908df26f923f79d6c2ed03251..43f4e07819aa07de1b51cc5272e11819319570bf 100644
--- a/nifty5/minimization/conjugate_gradient.py
+++ b/nifty5/minimization/conjugate_gradient.py
@@ -84,8 +84,7 @@ class ConjugateGradient(Minimizer):
logger.error("Error: ConjugateGradient: alpha<0.")
return energy, controller.ERROR
- q *= -alpha
- r = r + q
+ r = r - q*alpha
energy = energy.at_with_grad(energy.position - alpha*d, r)
@@ -103,7 +102,6 @@ class ConjugateGradient(Minimizer):
if status != controller.CONTINUE:
return energy, status
- d *= max(0, gamma/previous_gamma)
- d += s
+ d = d * max(0, gamma/previous_gamma) + s
previous_gamma = gamma
diff --git a/nifty5/minimization/l_bfgs.py b/nifty5/minimization/l_bfgs.py
index fda0e19404677fb65ae4db2c6a730214a6248e34..e2a75b2a1f5d4e8130883d0ba22216d90105e9fc 100644
--- a/nifty5/minimization/l_bfgs.py
+++ b/nifty5/minimization/l_bfgs.py
@@ -61,16 +61,16 @@ class L_BFGS(DescentMinimizer):
for i in range(k-1, k-nhist-1, -1):
idx = i % maxhist
alpha[idx] = s[idx].vdot(p)/s[idx].vdot(y[idx])
- p -= alpha[idx]*y[idx]
+ p = p - alpha[idx]*y[idx]
idx = (k-1) % maxhist
fact = s[idx].vdot(y[idx]) / y[idx].vdot(y[idx])
if fact <= 0.:
logger.error("L-BFGS curvature not positive definite!")
- p *= fact
+ p = p*fact
for i in range(k-nhist, k):
idx = i % maxhist
beta = y[idx].vdot(p) / s[idx].vdot(y[idx])
- p += (alpha[idx]-beta)*s[idx]
+ p = p + (alpha[idx]-beta)*s[idx]
self._lastx = x
self._lastgrad = gradient
self._k += 1
diff --git a/nifty5/minimization/vl_bfgs.py b/nifty5/minimization/vl_bfgs.py
index 67e67ca925dba209b8124805a40b52cd606f07b7..6ebde78248497f600431b3dc13576e6d83fa1434 100644
--- a/nifty5/minimization/vl_bfgs.py
+++ b/nifty5/minimization/vl_bfgs.py
@@ -67,7 +67,7 @@ class VL_BFGS(DescentMinimizer):
descent_direction = delta[0] * b[0]
for i in range(1, len(delta)):
- descent_direction += delta[i] * b[i]
+ descent_direction = descent_direction + delta[i]*b[i]
return descent_direction
diff --git a/nifty5/multi/multi_field.py b/nifty5/multi/multi_field.py
index 18011add3443c9d73c16ebc94b4dadc5bcb95c7e..cf2e5393cf40d187a2d9df0852a394992779db7d 100644
--- a/nifty5/multi/multi_field.py
+++ b/nifty5/multi/multi_field.py
@@ -220,3 +220,12 @@ for op in ["__add__", "__radd__",
return MultiField(result_val)
return func2
setattr(MultiField, op, func(op))
+
+for op in ["__iadd__", "__isub__", "__imul__", "__idiv__",
+ "__itruediv__", "__ifloordiv__", "__ipow__"]:
+ def func(op):
+ def func2(self, other):
+ raise TypeError(
+ "In-place operations are deliberately not supported")
+ return func2
+ setattr(MultiField, op, func(op))
diff --git a/nifty5/operators/diagonal_operator.py b/nifty5/operators/diagonal_operator.py
index b619083e9bec89fd91609c9996590bc1732c42bd..3412b562f5ce7da485a9f1335c11f1cace7fa305 100644
--- a/nifty5/operators/diagonal_operator.py
+++ b/nifty5/operators/diagonal_operator.py
@@ -185,7 +185,6 @@ class DiagonalOperator(EndomorphicOperator):
res = Field.from_random(random_type="normal", domain=self._domain,
dtype=dtype)
if from_inverse:
- res /= np.sqrt(self._ldiag)
+ return res/np.sqrt(self._ldiag)
else:
- res *= np.sqrt(self._ldiag)
- return res
+ return res*np.sqrt(self._ldiag)
diff --git a/nifty5/operators/fft_operator.py b/nifty5/operators/fft_operator.py
index 781b072f5bdf652fbc410ad7746b8af326ea64a7..be91ef5fad3ffbcab88c6ab696bda977ce686153 100644
--- a/nifty5/operators/fft_operator.py
+++ b/nifty5/operators/fft_operator.py
@@ -118,10 +118,7 @@ class FFTOperator(LinearOperator):
fct = self._domain[self._space].scalar_dvol
else:
fct = self._target[self._space].scalar_dvol
- if fct != 1:
- Tval *= fct
-
- return Tval
+ return Tval if fct == 1 else Tval*fct
@property
def domain(self):
diff --git a/nifty5/operators/sum_operator.py b/nifty5/operators/sum_operator.py
index 62c22bb8cb8c1842cc3d62acdc242127f6dd9f9a..5cbe8eb3de943eff8a0ddcd9f6afe855674f6f84 100644
--- a/nifty5/operators/sum_operator.py
+++ b/nifty5/operators/sum_operator.py
@@ -172,14 +172,15 @@ class SumOperator(LinearOperator):
def apply(self, x, mode):
self._check_mode(mode)
- for i, op in enumerate(self._ops):
- if i == 0:
- res = -op.apply(x, mode) if self._neg[i] else op.apply(x, mode)
+ res = None
+ for op, neg in zip(self._ops, self._neg):
+ if res is None:
+ res = -op.apply(x, mode) if neg else op.apply(x, mode)
else:
- if self._neg[i]:
- res -= op.apply(x, mode)
+ if neg:
+ res = res - op.apply(x, mode)
else:
- res += op.apply(x, mode)
+ res = res + op.apply(x, mode)
return res
def draw_sample(self, from_inverse=False, dtype=np.float64):
diff --git a/nifty5/probing/utils.py b/nifty5/probing/utils.py
index 72f653f18298a7dd6046f74988a6d738c3454e46..a1c909a07335b155b2b1420e66b24eb92d2e59c6 100644
--- a/nifty5/probing/utils.py
+++ b/nifty5/probing/utils.py
@@ -31,9 +31,9 @@ class StatCalculator(object):
self._M2 = 0.*value
else:
delta = value - self._mean
- self._mean += delta*(1./self._count)
+ self._mean = self.mean + delta*(1./self._count)
delta2 = value - self._mean
- self._M2 += delta*delta2
+ self._M2 = self._M2 + delta*delta2
@property
def mean(self):