From c3ed466f547cd9ee25fbe1ab84ecaf86e8a998a6 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Sun, 5 Aug 2018 14:00:28 +0200
Subject: [PATCH] no more chains
---
demos/Wiener_Filter.ipynb | 4 +--
demos/bernoulli_demo.py | 4 +--
demos/getting_started_1.py | 6 ++--
demos/getting_started_2.py | 6 ++--
demos/getting_started_3.py | 8 ++---
demos/polynomial_fit.py | 2 +-
nifty5/energies/hamiltonian.py | 2 +-
nifty5/energies/kl.py | 2 +-
nifty5/library/amplitude_model.py | 4 +--
nifty5/library/bernoulli_energy.py | 2 +-
nifty5/library/correlated_fields.py | 2 +-
nifty5/library/gaussian_energy.py | 2 +-
nifty5/library/poissonian_energy.py | 2 +-
nifty5/linearization.py | 32 +++++++++---------
nifty5/multi/block_diagonal_operator.py | 2 +-
.../operators/harmonic_smoothing_operator.py | 2 +-
nifty5/operators/linear_operator.py | 11 ++++---
nifty5/operators/operator.py | 33 ++++++-------------
nifty5/operators/sandwich_operator.py | 4 +--
nifty5/operators/smoothness_operator.py | 2 +-
nifty5/utilities.py | 13 ++++----
test/test_energies/test_map.py | 8 ++---
test/test_field.py | 4 +--
test/test_models/test_model_gradients.py | 6 ++--
test/test_multi_field.py | 2 +-
test/test_operators/test_adjoint.py | 2 +-
test/test_operators/test_composed_operator.py | 9 +++--
27 files changed, 82 insertions(+), 94 deletions(-)
diff --git a/demos/Wiener_Filter.ipynb b/demos/Wiener_Filter.ipynb
index dd8c7f374..8b6969725 100644
--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -429,7 +429,7 @@
"mask[l:h] = 0\n",
"mask = ift.Field.from_global_data(s_space, mask)\n",
"\n",
- "R = ift.DiagonalOperator(mask).chain(HT)\n",
+ "R = ift.DiagonalOperator(mask)(HT)\n",
"n = n.to_global_data_rw()\n",
"n[l:h] = 0\n",
"n = ift.Field.from_global_data(s_space, n)\n",
@@ -585,7 +585,7 @@
"mask[l:h,l:h] = 0.\n",
"mask = ift.Field.from_global_data(s_space, mask)\n",
"\n",
- "R = ift.DiagonalOperator(mask).chain(HT)\n",
+ "R = ift.DiagonalOperator(mask)(HT)\n",
"n = n.to_global_data_rw()\n",
"n[l:h, l:h] = 0\n",
"n = ift.Field.from_global_data(s_space, n)\n",
diff --git a/demos/bernoulli_demo.py b/demos/bernoulli_demo.py
index 7efd68a31..d517362c9 100644
--- a/demos/bernoulli_demo.py
+++ b/demos/bernoulli_demo.py
@@ -53,7 +53,7 @@ if __name__ == '__main__':
A = pd(a)
# Set up a sky model
- sky = HT.chain(ift.makeOp(A)).positive_tanh()
+ sky = HT(ift.makeOp(A)).positive_tanh()
GR = ift.GeometryRemover(position_space)
# Set up instrumental response
@@ -61,7 +61,7 @@ if __name__ == '__main__':
# Generate mock data
d_space = R.target[0]
- p = R.chain(sky)
+ p = R(sky)
mock_position = ift.from_random('normal', harmonic_space)
pp = p(mock_position)
data = np.random.binomial(1, pp.to_global_data().astype(np.float64))
diff --git a/demos/getting_started_1.py b/demos/getting_started_1.py
index 1e36c2c18..76425bea5 100644
--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -78,7 +78,7 @@ if __name__ == '__main__':
GR = ift.GeometryRemover(position_space)
mask = ift.Field.from_global_data(position_space, mask)
Mask = ift.DiagonalOperator(mask)
- R = GR.chain(Mask).chain(HT)
+ R = GR(Mask(HT))
data_space = GR.target
@@ -93,7 +93,7 @@ if __name__ == '__main__':
# Build propagator D and information source j
j = R.adjoint_times(N.inverse_times(data))
- D_inv = R.adjoint.chain(N.inverse).chain(R) + S.inverse
+ D_inv = R.adjoint(N.inverse(R)) + S.inverse
# Make it invertible
IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
D = ift.InversionEnabler(D_inv, IC, approximation=S.inverse).inverse
@@ -112,7 +112,7 @@ if __name__ == '__main__':
title="getting_started_1")
else:
ift.plot(HT(MOCK_SIGNAL), title='Mock Signal')
- ift.plot(mask_to_nan(mask, (GR.chain(Mask)).adjoint(data)),
+ ift.plot(mask_to_nan(mask, (GR(Mask)).adjoint(data)),
title='Data')
ift.plot(HT(m), title='Reconstruction')
ift.plot(mask_to_nan(mask, HT(m-MOCK_SIGNAL)), title='Residuals')
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index d878ec0df..c6622395e 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -70,16 +70,16 @@ if __name__ == '__main__':
A = pd(a)
# Set up a sky model
- sky = ift.exp(HT.chain(ift.makeOp(A)))
+ sky = ift.exp(HT(ift.makeOp(A)))
M = ift.DiagonalOperator(exposure)
GR = ift.GeometryRemover(position_space)
# Set up instrumental response
- R = GR.chain(M)
+ R = GR(M)
# Generate mock data
d_space = R.target[0]
- lamb = R.chain(sky)
+ lamb = R(sky)
mock_position = ift.from_random('normal', domain)
data = lamb(mock_position)
data = np.random.poisson(data.to_global_data().astype(np.float64))
diff --git a/demos/getting_started_3.py b/demos/getting_started_3.py
index dc6174a86..3aa3a0a5a 100644
--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -44,8 +44,8 @@ if __name__ == '__main__':
domain = ift.MultiDomain.union(
(A.domain, ift.MultiDomain.make({'xi': harmonic_space})))
- correlated_field = ht.chain(
- power_distributor.chain(A)*ift.FieldAdapter(domain, "xi"))
+ correlated_field = ht(
+ power_distributor(A)*ift.FieldAdapter(domain, "xi"))
# alternatively to the block above one can do:
# correlated_field = ift.CorrelatedField(position_space, A)
@@ -57,7 +57,7 @@ if __name__ == '__main__':
R = ift.LOSResponse(position_space, starts=LOS_starts,
ends=LOS_ends)
# build signal response model and model likelihood
- signal_response = R.chain(signal)
+ signal_response = R(signal)
# specify noise
data_space = R.target
noise = .001
@@ -69,7 +69,7 @@ if __name__ == '__main__':
# set up model likelihood
likelihood = ift.GaussianEnergy(
- mean=data, covariance=N).chain(signal_response)
+ mean=data, covariance=N)(signal_response)
# set up minimization and inversion schemes
ic_cg = ift.GradientNormController(iteration_limit=10)
diff --git a/demos/polynomial_fit.py b/demos/polynomial_fit.py
index 23ea6e726..c8eba5cec 100644
--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -97,7 +97,7 @@ d = ift.from_global_data(d_space, y)
N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
IC = ift.GradientNormController(tol_abs_gradnorm=1e-8)
-likelihood = ift.GaussianEnergy(d, N).chain(R)
+likelihood = ift.GaussianEnergy(d, N)(R)
H = ift.Hamiltonian(likelihood, IC)
H = ift.EnergyAdapter(params, H)
H = H.make_invertible(IC)
diff --git a/nifty5/energies/hamiltonian.py b/nifty5/energies/hamiltonian.py
index 48f4729ab..fb5a45176 100644
--- a/nifty5/energies/hamiltonian.py
+++ b/nifty5/energies/hamiltonian.py
@@ -40,7 +40,7 @@ class Hamiltonian(Operator):
def target(self):
return DomainTuple.scalar_domain()
- def __call__(self, x):
+ def apply(self, x):
if self._ic_samp is None or not isinstance(x, Linearization):
return self._lh(x) + self._prior(x)
else:
diff --git a/nifty5/energies/kl.py b/nifty5/energies/kl.py
index 6c9bb07fd..d513d1101 100644
--- a/nifty5/energies/kl.py
+++ b/nifty5/energies/kl.py
@@ -42,6 +42,6 @@ class SampledKullbachLeiblerDivergence(Operator):
def target(self):
return DomainTuple.scalar_domain()
- def __call__(self, x):
+ def apply(self, x):
return (my_sum(map(lambda v: self._h(x+v), self._res_samples)) *
(1./len(self._res_samples)))
diff --git a/nifty5/library/amplitude_model.py b/nifty5/library/amplitude_model.py
index 7716a42ba..c2e27ef1f 100644
--- a/nifty5/library/amplitude_model.py
+++ b/nifty5/library/amplitude_model.py
@@ -130,7 +130,7 @@ class AmplitudeModel(Operator):
cepstrum = create_cepstrum_amplitude_field(dof_space, kern)
ceps = makeOp(sqrt(cepstrum))
- self._smooth_op = sym.chain(qht).chain(ceps)
+ self._smooth_op = sym(qht(ceps))
self._keys = tuple(keys)
@property
@@ -141,7 +141,7 @@ class AmplitudeModel(Operator):
def target(self):
return self._target
- def __call__(self, x):
+ def apply(self, x):
smooth_spec = self._smooth_op(x[self._keys[0]])
phi = x[self._keys[1]] + self._norm_phi_mean
linear_spec = self._slope(phi)
diff --git a/nifty5/library/bernoulli_energy.py b/nifty5/library/bernoulli_energy.py
index 2ee3a0b53..5a6c33dee 100644
--- a/nifty5/library/bernoulli_energy.py
+++ b/nifty5/library/bernoulli_energy.py
@@ -39,7 +39,7 @@ class BernoulliEnergy(Operator):
def target(self):
return DomainTuple.scalar_domain()
- def __call__(self, x):
+ def apply(self, x):
x = self._p(x)
v = x.log().vdot(-self._d) - (1.-x).log().vdot(1.-self._d)
if not isinstance(x, Linearization):
diff --git a/nifty5/library/correlated_fields.py b/nifty5/library/correlated_fields.py
index 53c2cacb8..d139259b8 100644
--- a/nifty5/library/correlated_fields.py
+++ b/nifty5/library/correlated_fields.py
@@ -58,7 +58,7 @@ class CorrelatedField(Operator):
def target(self):
return self._ht.target
- def __call__(self, x):
+ def apply(self, x):
A = self._power_distributor(self._amplitude_model(x))
correlated_field_h = A * x["xi"]
correlated_field = self._ht(correlated_field_h)
diff --git a/nifty5/library/gaussian_energy.py b/nifty5/library/gaussian_energy.py
index 3c462f589..85a3511d5 100644
--- a/nifty5/library/gaussian_energy.py
+++ b/nifty5/library/gaussian_energy.py
@@ -55,7 +55,7 @@ class GaussianEnergy(Operator):
def target(self):
return DomainTuple.scalar_domain()
- def __call__(self, x):
+ def apply(self, x):
residual = x if self._mean is None else x-self._mean
icovres = residual if self._icov is None else self._icov(residual)
res = .5*residual.vdot(icovres)
diff --git a/nifty5/library/poissonian_energy.py b/nifty5/library/poissonian_energy.py
index 4260f9d6b..0a42ba93c 100644
--- a/nifty5/library/poissonian_energy.py
+++ b/nifty5/library/poissonian_energy.py
@@ -41,7 +41,7 @@ class PoissonianEnergy(Operator):
def target(self):
return DomainTuple.scalar_domain()
- def __call__(self, x):
+ def apply(self, x):
x = self._op(x)
res = x.sum() - x.log().vdot(self._d)
if not isinstance(x, Linearization):
diff --git a/nifty5/linearization.py b/nifty5/linearization.py
index 03985f05e..c9020c0cb 100644
--- a/nifty5/linearization.py
+++ b/nifty5/linearization.py
@@ -46,8 +46,8 @@ class Linearization(object):
def __neg__(self):
return Linearization(
- -self._val, self._jac.chain(-1),
- None if self._metric is None else self._metric.chain(-1))
+ -self._val, self._jac*(-1),
+ None if self._metric is None else self._metric*(-1))
def __add__(self, other):
if isinstance(other, Linearization):
@@ -77,24 +77,24 @@ class Linearization(object):
d2 = makeOp(other._val)
return Linearization(
self._val*other._val,
- d2.chain(self._jac) + d1.chain(other._jac))
+ d2(self._jac) + d1(other._jac))
if isinstance(other, (int, float, complex)):
# if other == 0:
# return ...
- met = None if self._metric is None else self._metric.chain(other)
- return Linearization(self._val*other, self._jac.chain(other), met)
+ met = None if self._metric is None else self._metric(other)
+ return Linearization(self._val*other, self._jac(other), met)
if isinstance(other, (Field, MultiField)):
d2 = makeOp(other)
- return Linearization(self._val*other, d2.chain(self._jac))
+ return Linearization(self._val*other, d2(self._jac))
raise TypeError
def __rmul__(self, other):
from .sugar import makeOp
if isinstance(other, (int, float, complex)):
- return Linearization(self._val*other, self._jac.chain(other))
+ return Linearization(self._val*other, self._jac(other))
if isinstance(other, (Field, MultiField)):
d1 = makeOp(other)
- return Linearization(self._val*other, d1.chain(self._jac))
+ return Linearization(self._val*other, d1(self._jac))
def vdot(self, other):
from .domain_tuple import DomainTuple
@@ -102,11 +102,11 @@ class Linearization(object):
if isinstance(other, (Field, MultiField)):
return Linearization(
Field(DomainTuple.scalar_domain(),self._val.vdot(other)),
- VdotOperator(other).chain(self._jac))
+ VdotOperator(other)(self._jac))
return Linearization(
Field(DomainTuple.scalar_domain(),self._val.vdot(other._val)),
- VdotOperator(self._val).chain(other._jac) +
- VdotOperator(other._val).chain(self._jac))
+ VdotOperator(self._val)(other._jac) +
+ VdotOperator(other._val)(self._jac))
def sum(self):
from .domain_tuple import DomainTuple
@@ -114,24 +114,24 @@ class Linearization(object):
from .sugar import full
return Linearization(
Field(DomainTuple.scalar_domain(), self._val.sum()),
- SumReductionOperator(self._jac.target).chain(self._jac))
+ SumReductionOperator(self._jac.target)(self._jac))
def exp(self):
tmp = self._val.exp()
- return Linearization(tmp, makeOp(tmp).chain(self._jac))
+ return Linearization(tmp, makeOp(tmp)(self._jac))
def log(self):
tmp = self._val.log()
- return Linearization(tmp, makeOp(1./self._val).chain(self._jac))
+ return Linearization(tmp, makeOp(1./self._val)(self._jac))
def tanh(self):
tmp = self._val.tanh()
- return Linearization(tmp, makeOp(1.-tmp**2).chain(self._jac))
+ return Linearization(tmp, makeOp(1.-tmp**2)(self._jac))
def positive_tanh(self):
tmp = self._val.tanh()
tmp2 = 0.5*(1.+tmp)
- return Linearization(tmp2, makeOp(0.5*(1.-tmp**2)).chain(self._jac))
+ return Linearization(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
def add_metric(self, metric):
return Linearization(self._val, self._jac, metric)
diff --git a/nifty5/multi/block_diagonal_operator.py b/nifty5/multi/block_diagonal_operator.py
index 41c03043b..9d176a353 100644
--- a/nifty5/multi/block_diagonal_operator.py
+++ b/nifty5/multi/block_diagonal_operator.py
@@ -68,7 +68,7 @@ class BlockDiagonalOperator(EndomorphicOperator):
def _combine_chain(self, op):
if self._domain is not op._domain:
raise ValueError("domain mismatch")
- res = tuple(v1.chain(v2) for v1, v2 in zip(self._ops, op._ops))
+ res = tuple(v1(v2) for v1, v2 in zip(self._ops, op._ops))
return BlockDiagonalOperator(self._domain, res)
def _combine_sum(self, op, selfneg, opneg):
diff --git a/nifty5/operators/harmonic_smoothing_operator.py b/nifty5/operators/harmonic_smoothing_operator.py
index ca40ce806..a1bfbac0b 100644
--- a/nifty5/operators/harmonic_smoothing_operator.py
+++ b/nifty5/operators/harmonic_smoothing_operator.py
@@ -67,4 +67,4 @@ def HarmonicSmoothingOperator(domain, sigma, space=None):
ddom = list(domain)
ddom[space] = codomain
diag = DiagonalOperator(kernel, ddom, space)
- return Hartley.inverse.chain(diag).chain(Hartley)
+ return Hartley.inverse(diag(Hartley))
diff --git a/nifty5/operators/linear_operator.py b/nifty5/operators/linear_operator.py
index 783065ffc..5b4bbf799 100644
--- a/nifty5/operators/linear_operator.py
+++ b/nifty5/operators/linear_operator.py
@@ -142,9 +142,6 @@ class LinearOperator(Operator):
from .chain_operator import ChainOperator
return ChainOperator.make([self, other2])
- def chain(self, other):
- return self.__matmul__(other)
-
def __rmatmul__(self, other):
if np.isscalar(other) and other == 1.:
return self
@@ -213,10 +210,14 @@ class LinearOperator(Operator):
def __call__(self, x):
"""Same as :meth:`times`"""
+ from ..field import Field
+ from ..multi.multi_field import MultiField
+ if isinstance(x, (Field, MultiField)):
+ return self.apply(x, self.TIMES)
from ..linearization import Linearization
if isinstance(x, Linearization):
- return Linearization(self(x._val), self.chain(x._jac))
- return self.apply(x, self.TIMES)
+ return Linearization(self(x._val), self(x._jac))
+ return self.__matmul__(x)
def times(self, x):
""" Applies the Operator to a given Field.
diff --git a/nifty5/operators/operator.py b/nifty5/operators/operator.py
index 61346cde3..55416578e 100644
--- a/nifty5/operators/operator.py
+++ b/nifty5/operators/operator.py
@@ -33,26 +33,13 @@ class Operator(NiftyMetaBase()):
return NotImplemented
return _OpProd.make((self, x))
- def chain(self, x):
- res = self.__matmul__(x)
- if res == NotImplemented:
- raise TypeError("operator expected")
- return res
+ def apply(self, x):
+ raise NotImplementedError
def __call__(self, x):
- """Returns transformed x
-
- Parameters
- ----------
- x : Linearization
- input
-
- Returns
- -------
- Linearization
- output
- """
- raise NotImplementedError
+ if isinstance(x, Operator):
+ return _OpChain.make((self, x))
+ return self.apply(x)
for f in ["sqrt", "exp", "log", "tanh", "positive_tanh"]:
@@ -78,7 +65,7 @@ class _FunctionApplier(Operator):
def target(self):
return self._domain
- def __call__(self, x):
+ def apply(self, x):
return getattr(x, self._funcname)()
@@ -117,7 +104,7 @@ class _OpChain(_CombinedOperator):
def target(self):
return self._ops[0].target
- def __call__(self, x):
+ def apply(self, x):
for op in reversed(self._ops):
x = op(x)
return x
@@ -135,7 +122,7 @@ class _OpProd(_CombinedOperator):
def target(self):
return self._ops[0].target
- def __call__(self, x):
+ def apply(self, x):
from ..utilities import my_product
return my_product(map(lambda op: op(x), self._ops))
@@ -154,7 +141,7 @@ class _OpSum(_CombinedOperator):
def target(self):
return self._target
- def __call__(self, x):
+ def apply(self, x):
raise NotImplementedError
@@ -193,7 +180,7 @@ class QuadraticFormOperator(Operator):
def target(self):
return self._target
- def __call__(self, x):
+ def apply(self, x):
if isinstance(x, Linearization):
jac = self._op(x)
val = Field(self._target, 0.5 * x.vdot(jac))
diff --git a/nifty5/operators/sandwich_operator.py b/nifty5/operators/sandwich_operator.py
index 20236a580..480e098db 100644
--- a/nifty5/operators/sandwich_operator.py
+++ b/nifty5/operators/sandwich_operator.py
@@ -56,9 +56,9 @@ class SandwichOperator(EndomorphicOperator):
raise TypeError("cheese must be a linear operator")
if cheese is None:
cheese = ScalingOperator(1., bun.target)
- op = bun.adjoint.chain(bun)
+ op = bun.adjoint(bun)
else:
- op = bun.adjoint.chain(cheese).chain(bun)
+ op = bun.adjoint(cheese(bun))
# if our sandwich is diagonal, we can return immediately
if isinstance(op, (ScalingOperator, DiagonalOperator)):
diff --git a/nifty5/operators/smoothness_operator.py b/nifty5/operators/smoothness_operator.py
index 981863927..38e068730 100644
--- a/nifty5/operators/smoothness_operator.py
+++ b/nifty5/operators/smoothness_operator.py
@@ -54,4 +54,4 @@ def SmoothnessOperator(domain, strength=1., logarithmic=True, space=None):
if strength == 0.:
return ScalingOperator(0., domain)
laplace = LaplaceOperator(domain, logarithmic=logarithmic, space=space)
- return (strength**2)*laplace.adjoint.chain(laplace)
+ return (strength**2)*laplace.adjoint(laplace)
diff --git a/nifty5/utilities.py b/nifty5/utilities.py
index 8bf84bc2c..d7436ccf8 100644
--- a/nifty5/utilities.py
+++ b/nifty5/utilities.py
@@ -23,6 +23,8 @@ from itertools import product
import numpy as np
from future.utils import with_metaclass
+import pyfftw
+from pyfftw.interfaces.numpy_fft import rfftn, fftn
from .compat import *
@@ -201,9 +203,11 @@ _fft_extra_args = dict(planner_effort='FFTW_ESTIMATE')
def fft_prep():
- import pyfftw
- pyfftw.interfaces.cache.enable()
- pyfftw.interfaces.cache.set_keepalive_time(1000.)
+ if not fft_prep._initialized:
+ pyfftw.interfaces.cache.enable()
+ pyfftw.interfaces.cache.set_keepalive_time(1000.)
+ fft_prep._initialized = True
+fft_prep._initialized = False
def hartley(a, axes=None):
@@ -214,7 +218,6 @@ def hartley(a, axes=None):
if iscomplextype(a.dtype):
raise TypeError("Hartley transform requires real-valued arrays.")
- from pyfftw.interfaces.numpy_fft import rfftn
tmp = rfftn(a, axes=axes, threads=nthreads(), **_fft_extra_args)
def _fill_array(tmp, res, axes):
@@ -258,7 +261,6 @@ def my_fftn_r2c(a, axes=None):
if iscomplextype(a.dtype):
raise TypeError("Transform requires real-valued input arrays.")
- from pyfftw.interfaces.numpy_fft import rfftn
tmp = rfftn(a, axes=axes, threads=nthreads(), **_fft_extra_args)
def _fill_complex_array(tmp, res, axes):
@@ -293,7 +295,6 @@ def my_fftn_r2c(a, axes=None):
def my_fftn(a, axes=None):
- from pyfftw.interfaces.numpy_fft import fftn
return fftn(a, axes=axes, **_fft_extra_args)
diff --git a/test/test_energies/test_map.py b/test/test_energies/test_map.py
index a88a4b272..ae53e8259 100644
--- a/test/test_energies/test_map.py
+++ b/test/test_energies/test_map.py
@@ -56,18 +56,18 @@ class Energy_Tests(unittest.TestCase):
def d_model():
if nonlinearity == "":
- return R.chain(ht.chain(ift.makeOp(A)))
+ return R(ht(ift.makeOp(A)))
else:
- tmp = ht.chain(ift.makeOp(A))
+ tmp = ht(ift.makeOp(A))
nonlin = getattr(tmp, nonlinearity)()
- return R.chain(nonlin)
+ return R(nonlin)
d = d_model()(xi0) + n
if noise == 1:
N = None
- energy = ift.GaussianEnergy(d, N).chain(d_model())
+ energy = ift.GaussianEnergy(d, N)(d_model())
if nonlinearity == "":
ift.extra.check_value_gradient_metric_consistency(
energy, xi0, ntries=10)
diff --git a/test/test_field.py b/test/test_field.py
index e08463c90..cfc6f7d0e 100644
--- a/test/test_field.py
+++ b/test/test_field.py
@@ -66,7 +66,7 @@ class Test_Functionality(unittest.TestCase):
op1 = ift.create_power_operator((space1, space2), _spec1, 0)
op2 = ift.create_power_operator((space1, space2), _spec2, 1)
- opfull = op2.chain(op1)
+ opfull = op2(op1)
samples = 500
sc1 = ift.StatCalculator()
@@ -94,7 +94,7 @@ class Test_Functionality(unittest.TestCase):
S_1 = ift.create_power_operator((space1, space2), _spec1, 0)
S_2 = ift.create_power_operator((space1, space2), _spec2, 1)
- S_full = S_2.chain(S_1)
+ S_full = S_2(S_1)
samples = 500
sc1 = ift.StatCalculator()
diff --git a/test/test_models/test_model_gradients.py b/test/test_models/test_model_gradients.py
index 8f1bdf839..6a3abd2d5 100644
--- a/test/test_models/test_model_gradients.py
+++ b/test/test_models/test_model_gradients.py
@@ -71,16 +71,16 @@ class Model_Tests(unittest.TestCase):
model = ift.FieldAdapter(dom, "s1")*3.
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
- model = ift.ScalingOperator(2.456, space).chain(
+ model = ift.ScalingOperator(2.456, space)(
ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2"))
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
- model = ift.positive_tanh(ift.ScalingOperator(2.456, space).chain(
+ model = ift.positive_tanh(ift.ScalingOperator(2.456, space)(
ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2")))
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
if isinstance(space, ift.RGSpace):
- model = ift.FFTOperator(space).chain(
+ model = ift.FFTOperator(space)(
ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2"))
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
diff --git a/test/test_multi_field.py b/test/test_multi_field.py
index f2dbdf082..d9854bded 100644
--- a/test/test_multi_field.py
+++ b/test/test_multi_field.py
@@ -40,7 +40,7 @@ class Test_Functionality(unittest.TestCase):
def test_blockdiagonal(self):
op = ift.BlockDiagonalOperator(
dom, (ift.ScalingOperator(20., dom["d1"]),))
- op2 = op.chain(op)
+ op2 = op(op)
ift.extra.consistency_check(op2)
assert_equal(type(op2), ift.BlockDiagonalOperator)
f1 = op2(ift.full(dom, 1))
diff --git a/test/test_operators/test_adjoint.py b/test/test_operators/test_adjoint.py
index e56ee11df..540ec34d8 100644
--- a/test/test_operators/test_adjoint.py
+++ b/test/test_operators/test_adjoint.py
@@ -53,7 +53,7 @@ class Consistency_Tests(unittest.TestCase):
dtype=dtype))
op = ift.SandwichOperator.make(a, b)
ift.extra.consistency_check(op, dtype, dtype)
- op = a.chain(b)
+ op = a(b)
ift.extra.consistency_check(op, dtype, dtype)
op = a+b
ift.extra.consistency_check(op, dtype, dtype)
diff --git a/test/test_operators/test_composed_operator.py b/test/test_operators/test_composed_operator.py
index 81bb1acd4..46a0cd8cf 100644
--- a/test/test_operators/test_composed_operator.py
+++ b/test/test_operators/test_composed_operator.py
@@ -37,7 +37,7 @@ class ComposedOperator_Tests(unittest.TestCase):
op1 = ift.DiagonalOperator(diag1, cspace, spaces=(0,))
op2 = ift.DiagonalOperator(diag2, cspace, spaces=(1,))
- op = op2.chain(op1)
+ op = op2(op1)
rand1 = ift.Field.from_random('normal', domain=(space1, space2))
rand2 = ift.Field.from_random('normal', domain=(space1, space2))
@@ -54,7 +54,7 @@ class ComposedOperator_Tests(unittest.TestCase):
op1 = ift.DiagonalOperator(diag1, cspace, spaces=(0,))
op2 = ift.DiagonalOperator(diag2, cspace, spaces=(1,))
- op = op2.chain(op1)
+ op = op2(op1)
rand1 = ift.Field.from_random('normal', domain=(space1, space2))
tt1 = op.inverse_times(op.times(rand1))
@@ -75,8 +75,7 @@ class ComposedOperator_Tests(unittest.TestCase):
def test_chain(self, space):
op1 = ift.makeOp(ift.Field.full(space, 2.))
op2 = 3.
- full_op = (op1.chain(op2).chain(op2).chain(op1).
- chain(op1).chain(op1).chain(op2))
+ full_op = op1(op2)(op2)(op1)(op1)(op1)(op2)
x = ift.Field.full(space, 1.)
res = full_op(x)
assert_equal(isinstance(full_op, ift.DiagonalOperator), True)
@@ -86,7 +85,7 @@ class ComposedOperator_Tests(unittest.TestCase):
def test_mix(self, space):
op1 = ift.makeOp(ift.Field.full(space, 2.))
op2 = 3.
- full_op = op1.chain(op2 + op2).chain(op1).chain(op1) - op1.chain(op2)
+ full_op = op1(op2+op2)(op1)(op1) - op1(op2)
x = ift.Field.full(space, 1.)
res = full_op(x)
assert_equal(isinstance(full_op, ift.DiagonalOperator), True)
--
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