Commit 85b3b288 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

Operators can no longer be chained by '*'; you need to use '.chain()' or '@' (in Python3)

parent b44405a4
......@@ -429,7 +429,7 @@
"mask[l:h] = 0\n",
"mask = ift.Field.from_global_data(s_space, mask)\n",
"\n",
"R = ift.DiagonalOperator(mask)*HT\n",
"R = ift.DiagonalOperator(mask).chain(HT)\n",
"n = n.to_global_data().copy()\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)*HT\n",
"R = ift.DiagonalOperator(mask).chain(HT)\n",
"n = n.to_global_data().copy()\n",
"n[l:h, l:h] = 0\n",
"n = ift.Field.from_global_data(s_space, n)\n",
......
......@@ -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 * Mask * HT
R = GR.chain(Mask).chain(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 * N.inverse * R + S.inverse
D_inv = R.adjoint.chain(N.inverse).chain(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 +112,8 @@ if __name__ == '__main__':
title="getting_started_1")
else:
ift.plot(HT(MOCK_SIGNAL), title='Mock Signal')
ift.plot(mask_to_nan(mask, (GR*Mask).adjoint(data)), title='Data')
ift.plot(mask_to_nan(mask, (GR.chain(Mask)).adjoint(data)),
title='Data')
ift.plot(HT(m), title='Reconstruction')
ift.plot(mask_to_nan(mask, HT(m-MOCK_SIGNAL)))
ift.plot_finish(nx=2, ny=2, xsize=10, ysize=10,
......
......@@ -75,7 +75,7 @@ if __name__ == '__main__':
M = ift.DiagonalOperator(exposure)
GR = ift.GeometryRemover(position_space)
# Set up instrumental response
R = GR * M
R = GR.chain(M)
# Generate mock data
d_space = R.target[0]
......
......@@ -23,7 +23,7 @@ class EnergyAdapter(Energy):
@property
def value(self):
if self._val is None:
self._val = self._op(self._position)
self._val = self._op(self._position)
return self._val
@property
......
......@@ -130,7 +130,7 @@ class AmplitudeModel(Operator):
cepstrum = create_cepstrum_amplitude_field(dof_space, kern)
ceps = makeOp(sqrt(cepstrum))
self._smooth_op = sym * qht * ceps
self._smooth_op = sym.chain(qht).chain(ceps)
self._keys = tuple(keys)
@property
......
......@@ -47,8 +47,8 @@ class Linearization(object):
def __neg__(self):
return Linearization(
-self._val, self._jac*(-1),
None if self._metric is None else self._metric*(-1))
-self._val, self._jac.chain(-1),
None if self._metric is None else self._metric.chain(-1))
def __add__(self, other):
if isinstance(other, Linearization):
......@@ -79,47 +79,49 @@ class Linearization(object):
d2 = makeOp(other._val)
return Linearization(
self._val*other._val,
RelaxedSumOperator((d2*self._jac, d1*other._jac)))
RelaxedSumOperator((d2.chain(self._jac),
d1.chain(other._jac))))
if isinstance(other, (int, float, complex)):
# if other == 0:
# return ...
met = None if self._metric is None else self._metric*other
return Linearization(self._val*other, self._jac*other, met)
met = None if self._metric is None else self._metric.chain(other)
return Linearization(self._val*other, self._jac.chain(other), met)
if isinstance(other, (Field, MultiField)):
d2 = makeOp(other)
return Linearization(self._val*other, d2*self._jac)
return Linearization(self._val*other, d2.chain(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*other)
return Linearization(self._val*other, self._jac.chain(other))
if isinstance(other, (Field, MultiField)):
d1 = makeOp(other)
return Linearization(self._val*other, d1*self._jac)
return Linearization(self._val*other, d1.chain(self._jac))
def sum(self):
from .sugar import full
from .operators.vdot_operator import VdotOperator
return Linearization(full((), self._val.sum()),
VdotOperator(full(self._jac.target, 1))*self._jac)
return Linearization(
full((), self._val.sum()),
VdotOperator(full(self._jac.target, 1)).chain(self._jac))
def exp(self):
tmp = self._val.exp()
return Linearization(tmp, makeOp(tmp)*self._jac)
return Linearization(tmp, makeOp(tmp).chain(self._jac))
def log(self):
tmp = self._val.log()
return Linearization(tmp, makeOp(1./self._val)*self._jac)
return Linearization(tmp, makeOp(1./self._val).chain(self._jac))
def tanh(self):
tmp = self._val.tanh()
return Linearization(tmp, makeOp(1.-tmp**2)*self._jac)
return Linearization(tmp, makeOp(1.-tmp**2).chain(self._jac))
def positive_tanh(self):
tmp = self._val.tanh()
tmp2 = 0.5*(1.+tmp)
return Linearization(tmp2, makeOp(0.5*(1.-tmp**2))*self._jac)
return Linearization(tmp2, makeOp(0.5*(1.-tmp**2)).chain(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*v2 for v1, v2 in zip(self._ops, op._ops))
res = tuple(v1.chain(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*diag*Hartley
return Hartley.inverse.chain(diag).chain(Hartley)
......@@ -117,18 +117,46 @@ class LinearOperator(Operator):
def __mul__(self, other):
from .chain_operator import ChainOperator
if np.isscalar(other) and other == 1.:
if not np.isscalar(other):
return Operator.__mul__(self, other)
if other == 1.:
return self
other = self._toOperator(other, self.domain)
from .scaling_operator import ScalingOperator
other = ScalingOperator(other, self.domain)
return ChainOperator.make([self, other])
def __rmul__(self, other):
from .chain_operator import ChainOperator
if np.isscalar(other) and other == 1.:
if not np.isscalar(other):
return Operator.__rmul__(self, other)
if other == 1.:
return self
other = self._toOperator(other, self.target)
from .scaling_operator import ScalingOperator
other = ScalingOperator(other, self.target)
return ChainOperator.make([other, self])
def __matmul__(self, other):
if np.isscalar(other) and other == 1.:
return self
other2 = self._toOperator(other, self.domain)
if other2 == NotImplemented:
return Operator.__matmul__(self, other)
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
other2 = self._toOperator(other, self.target)
if other2 == NotImplemented:
from .chain_operator import ChainOperator
return Operator.__rmatmul__(self, other)
from .chain_operator import ChainOperator
return ChainOperator.make([other2, self])
def __add__(self, other):
from .sum_operator import SumOperator
if np.isscalar(other) and other == 0.:
......@@ -190,7 +218,7 @@ class LinearOperator(Operator):
"""Same as :meth:`times`"""
from ..linearization import Linearization
if isinstance(x, Linearization):
return Linearization(self(x._val), self*x._jac)
return Linearization(self(x._val), self.chain(x._jac))
return self.apply(x, self.TIMES)
def times(self, x):
......
......@@ -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*bun
op = bun.adjoint.chain(bun)
else:
op = bun.adjoint*cheese*bun
op = bun.adjoint.chain(cheese).chain(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*laplace
return (strength**2)*laplace.adjoint.chain(laplace)
......@@ -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*op1
opfull = op2.chain(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*S_1
S_full = S_2.chain(S_1)
samples = 500
sc1 = ift.StatCalculator()
......
......@@ -36,33 +36,6 @@ class Model_Tests(unittest.TestCase):
return ift.Linearization.make_var(s)
raise ValueError('unknown type passed')
def make_model(self, type, **kwargs):
if type == 'Constant':
np.random.seed(kwargs['seed'])
S = ift.ScalingOperator(1., kwargs['space'])
s = S.draw_sample()
return ift.Constant(
ift.MultiField.from_dict({kwargs['space_key']: s}),
ift.MultiField.from_dict({kwargs['space_key']: s}))
elif type == 'Variable':
np.random.seed(kwargs['seed'])
S = ift.ScalingOperator(1., kwargs['space'])
s = S.draw_sample()
return ift.Variable(
ift.MultiField.from_dict({kwargs['space_key']: s}))
elif type == 'LinearModel':
return ift.LinearModel(
inp=kwargs['model'], lin_op=kwargs['lin_op'])
else:
raise ValueError('unknown type passed')
def make_linear_operator(self, type, **kwargs):
if type == 'ScalingOperator':
lin_op = ift.ScalingOperator(1., kwargs['space'])
else:
raise ValueError('unknown type passed')
return lin_op
@expand(product(
[ift.GLSpace(15),
ift.RGSpace(64, distances=.789),
......@@ -71,7 +44,7 @@ class Model_Tests(unittest.TestCase):
))
def testBasics(self, space, seed):
var = self.make_linearization("Variable", space, seed)
model = lambda inp: inp
model = ift.ScalingOperator(6., var.target)
ift.extra.check_value_gradient_consistency(model, var.val)
@expand(product(
......@@ -89,17 +62,17 @@ class Model_Tests(unittest.TestCase):
lin2 = self.make_linearization(type2, dom2, seed)
dom = ift.MultiDomain.union((dom1, dom2))
model = lambda inp: inp["s1"]*inp["s2"]
model = ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2")
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
model = lambda inp: inp["s1"]+inp["s2"]
model = ift.FieldAdapter(dom, "s1")+ift.FieldAdapter(dom, "s2")
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
model = lambda inp: inp["s1"]*3.
pos = ift.from_random("normal", dom1)
model = ift.FieldAdapter(dom, "s1")*3.
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
model = lambda inp: ift.ScalingOperator(2.456, space)(
inp["s1"]*inp["s2"])
model = ift.ScalingOperator(2.456, space).chain(
ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2"))
pos = ift.from_random("normal", dom)
ift.extra.check_value_gradient_consistency(model, pos)
model = lambda inp: ift.ScalingOperator(2.456, space)(
......
......@@ -40,7 +40,7 @@ class Test_Functionality(unittest.TestCase):
def test_blockdiagonal(self):
op = ift.BlockDiagonalOperator(
dom, (ift.ScalingOperator(20., dom["d1"]),))
op2 = op*op
op2 = op.chain(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*b
op = a.chain(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*op1
op = op2.chain(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*op1
op = op2.chain(op1)
rand1 = ift.Field.from_random('normal', domain=(space1, space2))
tt1 = op.inverse_times(op.times(rand1))
......@@ -75,7 +75,8 @@ class ComposedOperator_Tests(unittest.TestCase):
def test_chain(self, space):
op1 = ift.makeOp(ift.Field.full(space, 2.))
op2 = 3.
full_op = op1 * op2 * (op2 * op1) * op1 * op1 * op2
full_op = (op1.chain(op2).chain(op2).chain(op1).
chain(op1).chain(op1).chain(op2))
x = ift.Field.full(space, 1.)
res = full_op(x)
assert_equal(isinstance(full_op, ift.DiagonalOperator), True)
......@@ -85,7 +86,7 @@ class ComposedOperator_Tests(unittest.TestCase):
def test_mix(self, space):
op1 = ift.makeOp(ift.Field.full(space, 2.))
op2 = 3.
full_op = op1 * (op2 + op2) * op1 * op1 - op1 * op2
full_op = op1.chain(op2 + op2).chain(op1).chain(op1) - op1.chain(op2)
x = ift.Field.full(space, 1.)
res = full_op(x)
assert_equal(isinstance(full_op, ift.DiagonalOperator), True)
......
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