diff --git a/demos/Wiener_Filter.ipynb b/demos/Wiener_Filter.ipynb
index 1754210855c62bbedd755e0db45f734f626cab0e..b0ef1f8b38a3e372d0c120a45bceaabe287e35c0 100644
--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -240,7 +240,7 @@
"sh = Sh.draw_sample()\n",
"noiseless_data=R(sh)\n",
"noise_amplitude = np.sqrt(0.2)\n",
- "N = ift.ScalingOperator(noise_amplitude**2, s_space)\n",
+ "N = ift.ScalingOperator(s_space, noise_amplitude**2)\n",
"\n",
"n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
" std=noise_amplitude, mean=0)\n",
@@ -391,7 +391,7 @@
"source": [
"# Operators\n",
"Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
- "N = ift.ScalingOperator(noise_amplitude**2,s_space)\n",
+ "N = ift.ScalingOperator(s_space, noise_amplitude**2)\n",
"# R is defined below\n",
"\n",
"# Fields\n",
@@ -569,7 +569,7 @@
"\n",
"# Operators\n",
"Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
- "N = ift.ScalingOperator(sigma2,s_space)\n",
+ "N = ift.ScalingOperator(s_space, sigma2)\n",
"\n",
"# Fields and data\n",
"sh = Sh.draw_sample()\n",
diff --git a/demos/getting_started_1.py b/demos/getting_started_1.py
index 3698549a02ba2640b895fdf6019824a90c8f7537..886a4ceaa7362f7ae73d90a92221536e45e359d1 100644
--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -113,7 +113,7 @@ if __name__ == '__main__':
# Set the noise covariance N
noise = 5.
- N = ift.ScalingOperator(noise, data_space)
+ N = ift.ScalingOperator(data_space, noise)
# Create mock data
MOCK_SIGNAL = S.draw_sample()
diff --git a/demos/getting_started_3.py b/demos/getting_started_3.py
index 2032c6b8c1dc442d53df0cdecbdcfaaf24836e09..c3fdbb83b0206ec923f29366879399bc1910ccbd 100644
--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -74,7 +74,7 @@ if __name__ == '__main__':
# Specify noise
data_space = R.target
noise = .001
- N = ift.ScalingOperator(noise, data_space)
+ N = ift.ScalingOperator(data_space, noise)
# Generate mock signal and data
mock_position = ift.from_random('normal', signal_response.domain)
diff --git a/demos/getting_started_mf.py b/demos/getting_started_mf.py
index d02ba63c73d9f0ff13dd31dfc1d01054435f9fb8..1883f9a13433528f7055b64e736b06c55819dacb 100644
--- a/demos/getting_started_mf.py
+++ b/demos/getting_started_mf.py
@@ -93,7 +93,7 @@ if __name__ == '__main__':
# Specify noise
data_space = R.target
noise = .001
- N = ift.ScalingOperator(noise, data_space)
+ N = ift.ScalingOperator(data_space, noise)
# Generate mock signal and data
mock_position = ift.from_random('normal', signal_response.domain)
diff --git a/nifty6/library/adjust_variances.py b/nifty6/library/adjust_variances.py
index e83c693f6a9ab21a5aefaa37358f232d6a84be61..1f3c624e897ae6312383a0e6f4be3b1b842cca57 100644
--- a/nifty6/library/adjust_variances.py
+++ b/nifty6/library/adjust_variances.py
@@ -72,7 +72,7 @@ def make_adjust_variances_hamiltonian(a,
x = (a.conjugate()*a).real
if scaling is not None:
- x = ScalingOperator(scaling, x.target)(x)
+ x = ScalingOperator(x.target, scaling)(x)
return StandardHamiltonian(InverseGammaLikelihood(d_eval/2.)(x),
ic_samp=ic_samp)
diff --git a/nifty6/library/dynamic_operator.py b/nifty6/library/dynamic_operator.py
index 8a16ae3eae9ff4b038463a6d0a7184bd1a57a95c..32f991141acba990ae1fb2392eecd3ec89e1c803 100644
--- a/nifty6/library/dynamic_operator.py
+++ b/nifty6/library/dynamic_operator.py
@@ -74,7 +74,7 @@ def _make_dynamic_operator(target,
ops['FFT'] = FFT
ops['Real'] = Real
if harmonic_padding is None:
- CentralPadd = ScalingOperator(1., FFT.target)
+ CentralPadd = ScalingOperator(FFT.target, 1.)
else:
if isinstance(harmonic_padding, int):
harmonic_padding = list((harmonic_padding,)*len(FFT.target.shape))
@@ -123,7 +123,7 @@ def _make_dynamic_operator(target,
c = FieldAdapter(UnstructuredDomain(len(sigc)), keys[1])
c = makeOp(Field(c.target, np.array(sigc)))(c)
- lightspeed = ScalingOperator(-0.5, c.target)(c).exp()
+ lightspeed = ScalingOperator(c.target, -0.5)(c).exp()
scaling = np.array(m.target[0].distances[1:])/m.target[0].distances[0]
scaling = DiagonalOperator(Field(c.target, scaling))
ops['lightspeed'] = scaling(lightspeed)
diff --git a/nifty6/linearization.py b/nifty6/linearization.py
index d62d5b402fb7731874c73cbc5966011192ef25a2..8b1787056eea1b040b5e661f370aaf5fc235bbe7 100644
--- a/nifty6/linearization.py
+++ b/nifty6/linearization.py
@@ -406,7 +406,7 @@ class Linearization(object):
the requested Linearization
"""
from .operators.scaling_operator import ScalingOperator
- return Linearization(field, ScalingOperator(1., field.domain),
+ return Linearization(field, ScalingOperator(field.domain, 1.),
want_metric=want_metric)
@staticmethod
@@ -492,7 +492,7 @@ class Linearization(object):
if len(constants) == 0:
return Linearization.make_var(field, want_metric)
else:
- ops = {key: ScalingOperator(0. if key in constants else 1., dom)
+ ops = {key: ScalingOperator(dom, 0. if key in constants else 1.)
for key, dom in field.domain.items()}
bdop = BlockDiagonalOperator(field.domain, ops)
return Linearization(field, bdop, want_metric=want_metric)
diff --git a/nifty6/operators/chain_operator.py b/nifty6/operators/chain_operator.py
index af46f4fefe5d1914745fd8164bbdb6549b05ed2a..78d83ef820595e1ac49b5a0dd30b8c07c1be21a0 100644
--- a/nifty6/operators/chain_operator.py
+++ b/nifty6/operators/chain_operator.py
@@ -72,7 +72,7 @@ class ChainOperator(LinearOperator):
break
if fct != 1 or len(opsnew) == 0:
# have to add the scaling operator at the end
- opsnew.append(ScalingOperator(fct, lastdom))
+ opsnew.append(ScalingOperator(lastdom, fct))
ops = opsnew
# combine DiagonalOperators where possible
opsnew = []
diff --git a/nifty6/operators/energy_operators.py b/nifty6/operators/energy_operators.py
index 221d70e4bbd062aa043684c14303978746731844..b748a1fccb142a4e9a55fef5063e596cda855568 100644
--- a/nifty6/operators/energy_operators.py
+++ b/nifty6/operators/energy_operators.py
@@ -276,7 +276,7 @@ class StudentTEnergy(EnergyOperator):
return Field.scalar(v)
if not x.want_metric:
return v
- met = ScalingOperator((self._theta+1)/(self._theta+3), self.domain)
+ met = ScalingOperator(self.domain, (self._theta+1) / (self._theta+3))
met = SandwichOperator.make(x.jac, met)
return v.add_metric(met)
diff --git a/nifty6/operators/harmonic_operators.py b/nifty6/operators/harmonic_operators.py
index bd4a5e33739e173e34be6d55486d186faf9490d1..76b6f1157378a3a3546a9dcf39c645c20bf5458e 100644
--- a/nifty6/operators/harmonic_operators.py
+++ b/nifty6/operators/harmonic_operators.py
@@ -345,7 +345,7 @@ def HarmonicSmoothingOperator(domain, sigma, space=None):
if sigma < 0.:
raise ValueError("sigma must be non-negative")
if sigma == 0.:
- return ScalingOperator(1., domain)
+ return ScalingOperator(domain, 1.)
domain = DomainTuple.make(domain)
space = utilities.infer_space(domain, space)
diff --git a/nifty6/operators/operator.py b/nifty6/operators/operator.py
index 4790da315d6925a39c2b60643e1005d035419cd0..4d2ebcc51d4e851ecf0f23ca230fda50d967d445 100644
--- a/nifty6/operators/operator.py
+++ b/nifty6/operators/operator.py
@@ -60,7 +60,7 @@ class Operator(metaclass=NiftyMeta):
if factor == 1:
return self
from .scaling_operator import ScalingOperator
- return ScalingOperator(factor, self.target)(self)
+ return ScalingOperator(self.target, factor)(self)
def conjugate(self):
from .simple_linear_operators import ConjugationOperator
diff --git a/nifty6/operators/sandwich_operator.py b/nifty6/operators/sandwich_operator.py
index cfc4333efd92686976148838f8c0a06d20315d9f..07a451261dac4f85e666e69aeb6c25bf06c603bc 100644
--- a/nifty6/operators/sandwich_operator.py
+++ b/nifty6/operators/sandwich_operator.py
@@ -57,7 +57,7 @@ class SandwichOperator(EndomorphicOperator):
if cheese is not None and not isinstance(cheese, LinearOperator):
raise TypeError("cheese must be a linear operator or None")
if cheese is None:
- cheese = ScalingOperator(1., bun.target)
+ cheese = ScalingOperator(bun.target, 1.)
op = bun.adjoint(bun)
else:
op = bun.adjoint(cheese(bun))
diff --git a/nifty6/operators/scaling_operator.py b/nifty6/operators/scaling_operator.py
index 688b338a0b5776ff0eac4d99885d910fde27fff8..81e4ffb062a10ea76198d358162063460490eeaa 100644
--- a/nifty6/operators/scaling_operator.py
+++ b/nifty6/operators/scaling_operator.py
@@ -26,10 +26,10 @@ class ScalingOperator(EndomorphicOperator):
Parameters
----------
- factor : scalar
- The multiplication factor
domain : Domain or tuple of Domain or DomainTuple
The domain on which the Operator's input Field is defined.
+ factor : scalar
+ The multiplication factor
Notes
-----
@@ -50,13 +50,13 @@ class ScalingOperator(EndomorphicOperator):
somewhere else.
"""
- def __init__(self, factor, domain):
+ def __init__(self, domain, factor):
from ..sugar import makeDomain
if not np.isscalar(factor):
raise TypeError("Scalar required")
- self._factor = factor
self._domain = makeDomain(domain)
+ self._factor = factor
self._capability = self._all_ops
def apply(self, x, mode):
@@ -81,7 +81,7 @@ class ScalingOperator(EndomorphicOperator):
fct = np.conj(fct)
if trafo & self.INVERSE_BIT:
fct = 1./fct
- return ScalingOperator(fct, self._domain)
+ return ScalingOperator(self._domain, fct)
def _get_fct(self, from_inverse):
fct = self._factor
diff --git a/nifty6/operators/sum_operator.py b/nifty6/operators/sum_operator.py
index ca8a76847a41883ab86f72e2f14b3422eef4bdb4..d2b06be7c3e563598a605e07636553dfd0c83580 100644
--- a/nifty6/operators/sum_operator.py
+++ b/nifty6/operators/sum_operator.py
@@ -99,7 +99,7 @@ class SumOperator(LinearOperator):
break
if sum != 0 or len(opsnew) == 0:
# have to add the scaling operator at the end
- opsnew.append(ScalingOperator(sum, lastdom))
+ opsnew.append(ScalingOperator(lastdom, sum))
negnew.append(False)
ops = opsnew
diff --git a/nifty6/sugar.py b/nifty6/sugar.py
index 6341bbc47943376bdca2eaee9e05731d9e8c98e9..7b0318246e35629706cd052108b3db2a72ee9975 100644
--- a/nifty6/sugar.py
+++ b/nifty6/sugar.py
@@ -493,7 +493,7 @@ def calculate_position(operator, output):
if output.domain != operator.target:
raise TypeError
cov = 1e-3*output.val.max()**2
- invcov = ScalingOperator(cov, output.domain).inverse
+ invcov = ScalingOperator(output.domain, cov).inverse
d = output + invcov.draw_sample(from_inverse=True)
lh = GaussianEnergy(d, invcov)(operator)
H = StandardHamiltonian(
diff --git a/test/test_energy_gradients.py b/test/test_energy_gradients.py
index 9797ad8d91a268c2f5fb31666525d4b4b2c6f711..6d295d9d46356bb67d242931b9b89ef435a1d4bd 100644
--- a/test/test_energy_gradients.py
+++ b/test/test_energy_gradients.py
@@ -37,7 +37,7 @@ PARAMS = product(SEEDS, SPACES)
@pytest.fixture(params=PARAMS)
def field(request):
np.random.seed(request.param[0])
- S = ift.ScalingOperator(1., request.param[1])
+ S = ift.ScalingOperator(request.param[1], 1.)
s = S.draw_sample()
return ift.MultiField.from_dict({'s1': s})['s1']
@@ -76,7 +76,7 @@ def test_hamiltonian_and_KL(field):
lh = ift.GaussianEnergy(domain=space)
hamiltonian = ift.StandardHamiltonian(lh)
ift.extra.check_jacobian_consistency(hamiltonian, field)
- S = ift.ScalingOperator(1., space)
+ S = ift.ScalingOperator(space, 1.)
samps = [S.draw_sample() for i in range(3)]
kl = ift.AveragedEnergy(hamiltonian, samps)
ift.extra.check_jacobian_consistency(kl, field)
diff --git a/test/test_gaussian_energy.py b/test/test_gaussian_energy.py
index c802187ebd5aeea5d0a715ee6b8e4410432ef147..e317aac151c864fb6985baec2ddf9a511e431ef4 100644
--- a/test/test_gaussian_energy.py
+++ b/test/test_gaussian_energy.py
@@ -51,9 +51,9 @@ def test_gaussian_energy(space, nonlinearity, noise, seed):
pspec = ift.PS_field(pspace, pspec)
A = Dist(ift.sqrt(pspec))
- N = ift.ScalingOperator(noise, space)
+ N = ift.ScalingOperator(space, noise)
n = N.draw_sample()
- R = ift.ScalingOperator(10., space)
+ R = ift.ScalingOperator(space, 10.)
def d_model():
if nonlinearity == "":
diff --git a/test/test_multi_field.py b/test/test_multi_field.py
index e29fd31873b1d03fcd83e60a195d3dce7d44c946..5d06f6fd5316e9e61412285695165f0457e5bc6a 100644
--- a/test/test_multi_field.py
+++ b/test/test_multi_field.py
@@ -58,7 +58,7 @@ def test_dataconv():
def test_blockdiagonal():
op = ift.BlockDiagonalOperator(
- dom, {"d1": ift.ScalingOperator(20., dom["d1"])})
+ dom, {"d1": ift.ScalingOperator(dom["d1"], 20.)})
op2 = op(op)
ift.extra.consistency_check(op2)
assert_equal(type(op2), ift.BlockDiagonalOperator)
diff --git a/test/test_operators/test_composed_operator.py b/test/test_operators/test_composed_operator.py
index cfe587dc02d44cf997076092580465bcccc75dd0..15d93db5bf4e64af58cdcad3913dbf134e6459cc 100644
--- a/test/test_operators/test_composed_operator.py
+++ b/test/test_operators/test_composed_operator.py
@@ -65,7 +65,7 @@ def test_times_inverse_times(space1, space2):
def test_sum(space1):
op1 = ift.makeOp(ift.Field.full(space1, 2.))
- op2 = ift.ScalingOperator(3., space1)
+ op2 = ift.ScalingOperator(space1, 3.)
full_op = op1 + op2 - (op2 - op1) + op1 + op1 + op2
x = ift.Field.full(space1, 1.)
res = full_op(x)
@@ -75,7 +75,7 @@ def test_sum(space1):
def test_chain(space1):
op1 = ift.makeOp(ift.Field.full(space1, 2.))
- op2 = ift.ScalingOperator(3., space1)
+ op2 = ift.ScalingOperator(space1, 3.,)
full_op = op1(op2)(op2)(op1)(op1)(op1)(op2)
x = ift.Field.full(space1, 1.)
res = full_op(x)
@@ -85,7 +85,7 @@ def test_chain(space1):
def test_mix(space1):
op1 = ift.makeOp(ift.Field.full(space1, 2.))
- op2 = ift.ScalingOperator(3., space1)
+ op2 = ift.ScalingOperator(space1, 3.)
full_op = op1(op2 + op2)(op1)(op1) - op1(op2)
x = ift.Field.full(space1, 1.)
res = full_op(x)
diff --git a/test/test_operators/test_jacobian.py b/test/test_operators/test_jacobian.py
index 48a9feb21fed88e980aa840b6cf16c4464651cb1..7228574884e4a0ac583ca2b9112d8864cd7e4dad 100644
--- a/test/test_operators/test_jacobian.py
+++ b/test/test_operators/test_jacobian.py
@@ -39,10 +39,10 @@ seed = list2fixture([4, 78, 23])
def testBasics(space, seed):
np.random.seed(seed)
- S = ift.ScalingOperator(1., space)
+ S = ift.ScalingOperator(space, 1.)
s = S.draw_sample()
var = ift.Linearization.make_var(s)
- model = ift.ScalingOperator(6., var.target)
+ model = ift.ScalingOperator(var.target, 6.)
ift.extra.check_jacobian_consistency(model, var.val)
@@ -64,7 +64,7 @@ def testBinary(type1, type2, space, seed):
model = select_s1.scale(3.)
pos = ift.from_random("normal", dom1)
ift.extra.check_jacobian_consistency(model, pos, ntries=20)
- model = ift.ScalingOperator(2.456, space)(select_s1*select_s2)
+ model = ift.ScalingOperator(space, 2.456)(select_s1*select_s2)
pos = ift.from_random("normal", dom)
ift.extra.check_jacobian_consistency(model, pos, ntries=20)
model = ift.sigmoid(2.456*(select_s1*select_s2))
@@ -90,7 +90,7 @@ def testBinary(type1, type2, space, seed):
def testPointModel(space, seed):
- S = ift.ScalingOperator(1., space)
+ S = ift.ScalingOperator(space, 1.)
pos = S.draw_sample()
alpha = 1.5
q = 0.73
@@ -118,7 +118,7 @@ def testDynamicModel(target, causal, minimum_phase, seed):
'minimum_phase': minimum_phase
}
model, _ = ift.dynamic_operator(**dct)
- S = ift.ScalingOperator(1., model.domain)
+ S = ift.ScalingOperator(model.domain, 1.)
pos = S.draw_sample()
# FIXME I dont know why smaller tol fails for 3D example
ift.extra.check_jacobian_consistency(model, pos, tol=1e-5, ntries=20)
@@ -139,7 +139,7 @@ def testDynamicModel(target, causal, minimum_phase, seed):
dct['sigc'] = 1.
dct['quant'] = 5
model, _ = ift.dynamic_lightcone_operator(**dct)
- S = ift.ScalingOperator(1., model.domain)
+ S = ift.ScalingOperator(model.domain, 1.)
pos = S.draw_sample()
# FIXME I dont know why smaller tol fails for 3D example
ift.extra.check_jacobian_consistency(