Commit c3ed466f authored by Martin Reinecke's avatar Martin Reinecke

no more chains

parent 369c6e7c
......@@ -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",
......
......@@ -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))
......
......@@ -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')
......
......@@ -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))
......
......@@ -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)
......
......@@ -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)
......
......@@ -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:
......
......@@ -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)))
......@@ -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)
......
......@@ -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):
......
......@@ -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)
......
......@@ -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)
......
......@@ -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):
......
......@@ -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)
......
......@@ -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):
......
......@@ -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))
......@@ -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.
......
......@@ -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))
......
......@@ -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)):
......
......@@ -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)
......@@ -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)
......
......@@ -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)
......
......@@ -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()
......
......@@ -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)
......
......@@ -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))
......
......@@ -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)
......
......@@ -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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