rework pointwise operations

nifty6/domains/rg_space.py

@@ -142,8 +142,7 @@ class RGSpace(StructuredDomain):
 
     @staticmethod
     def _kernel(x, sigma):
-        from ..sugar import exp
-        return exp(x*x * (-2.*np.pi*np.pi*sigma*sigma))
+        return (x*x * (-2.*np.pi*np.pi*sigma*sigma)).ptw("exp")
 
     def get_fft_smoothing_kernel_function(self, sigma):
         if (not self.harmonic):

nifty6/field.py

@@ -634,10 +634,9 @@ class Field(object):
         Field
             The result of the operation.
         """
-        from .sugar import sqrt
         if self.scalar_weight(spaces) is not None:
             return self._contraction_helper('std', spaces)
-        return sqrt(self.var(spaces))
+        return self.var(spaces).ptw("sqrt")
 
     def s_std(self):
         """Determines the standard deviation of the Field.
@@ -677,17 +676,11 @@ class Field(object):
     def flexible_addsub(self, other, neg):
         return self-other if neg else self+other
 
-    def sigmoid(self):
-        return 0.5*(1.+self.tanh())
-
     def clip(self, min=None, max=None):
         min = min.val if isinstance(min, Field) else min
         max = max.val if isinstance(max, Field) else max
         return Field(self._domain, np.clip(self._val, min, max))
 
-    def one_over(self):
-        return 1/self
-
     def _binary_op(self, other, op):
         # if other is a field, make sure that the domains match
         f = getattr(self._val, op)
@@ -699,6 +692,13 @@ class Field(object):
             return Field(self._domain, f(other))
         return NotImplemented
 
+    def ptw(self, op, with_deriv=False):
+        from .pointwise import ptw_dict
+        if with_deriv:
+            tmp = ptw_dict[op][1](self._val)
+            return (Field(self._domain, tmp[0]),
+                    Field(self._domain, tmp[1]))
+        return Field(self._domain, ptw_dict[op][0](self._val))
 
 for op in ["__add__", "__radd__",
            "__sub__", "__rsub__",
@@ -721,11 +721,3 @@ for op in ["__iadd__", "__isub__", "__imul__", "__idiv__",
                 "In-place operations are deliberately not supported")
         return func2
     setattr(Field, op, func(op))
-
-for f in ["sqrt", "exp", "log", "sin", "cos", "tan", "sinh", "cosh", "tanh",
-          "absolute", "sinc", "sign", "log10", "log1p", "expm1"]:
-    def func(f):
-        def func2(self):
-            return Field(self._domain, getattr(np, f)(self.val))
-        return func2
-    setattr(Field, f, func(f))

nifty6/library/correlated_fields.py

@@ -126,7 +126,7 @@ class _LognormalMomentMatching(Operator):
         logmean, logsig = _lognormal_moments(mean, sig, N_copies)
         self._mean = mean
         self._sig = sig
-        op = _normal(logmean, logsig, key, N_copies).exp()
+        op = _normal(logmean, logsig, key, N_copies).ptw("exp")
         self._domain, self._target = op.domain, op.target
         self.apply = op.apply
 
@@ -224,8 +224,8 @@ class _Normalization(Operator):
 
     def apply(self, x):
         self._check_input(x)
-        amp = x.exp()
-        spec = (2*x).exp()
+        amp = x.ptw("exp")
+        spec = amp*amp
         # FIXME This normalizes also the zeromode which is supposed to be left
         # untouched by this operator
         return self._specsum(self._mode_multiplicity(spec))**(-0.5)*amp
@@ -332,17 +332,17 @@ class _Amplitude(Operator):
         sig_fluc = vol1 @ ps_expander @ fluctuations
 
         xi = ducktape(dom, None, key)
-        sigma = sig_flex*(Adder(shift) @ sig_asp).sqrt()
+        sigma = sig_flex*(Adder(shift) @ sig_asp).ptw("sqrt")
         smooth = _SlopeRemover(target, space) @ twolog @ (sigma*xi)
         op = _Normalization(target, space) @ (slope + smooth)
         if N_copies > 0:
             op = Distributor @ op
             sig_fluc = Distributor @ sig_fluc
-            op = Adder(Distributor(vol0)) @ (sig_fluc*(azm_expander @ azm.one_over())*op)
+            op = Adder(Distributor(vol0)) @ (sig_fluc*(azm_expander @ azm.ptw("reciprocal"))*op)
             self._fluc = (_Distributor(dofdex, fluctuations.target,
                                        distributed_tgt[0]) @ fluctuations)
         else:
-            op = Adder(vol0) @ (sig_fluc*(azm_expander @ azm.one_over())*op)
+            op = Adder(vol0) @ (sig_fluc*(azm_expander @ azm.ptw("reciprocal"))*op)
             self._fluc = fluctuations
 
         self.apply = op.apply
@@ -527,7 +527,7 @@ class CorrelatedFieldMaker:
             for _ in range(prior_info):
                 sc.add(op(from_random('normal', op.domain)))
             mean = sc.mean.val
-            stddev = sc.var.sqrt().val
+            stddev = sc.var.ptw("sqrt").val
             for m, s in zip(mean.flatten(), stddev.flatten()):
                 logger.info('{}: {:.02E} ± {:.02E}'.format(kk, m, s))
 
@@ -539,7 +539,7 @@ class CorrelatedFieldMaker:
         from ..sugar import from_random
         scm = 1.
         for a in self._a:
-            op = a.fluctuation_amplitude*self._azm.one_over()
+            op = a.fluctuation_amplitude*self._azm.ptw("reciprocal")
             res = np.array([op(from_random('normal', op.domain)).val
                             for _ in range(nsamples)])
             scm *= res**2 + 1.
@@ -573,9 +573,9 @@ class CorrelatedFieldMaker:
             return self.average_fluctuation(0)
         q = 1.
         for a in self._a:
-            fl = a.fluctuation_amplitude*self._azm.one_over()
+            fl = a.fluctuation_amplitude*self._azm.ptw("reciprocal")
             q = q*(Adder(full(fl.target, 1.)) @ fl**2)
-        return (Adder(full(q.target, -1.)) @ q).sqrt()*self._azm
+        return (Adder(full(q.target, -1.)) @ q).ptw("sqrt")*self._azm
 
     def slice_fluctuation(self, space):
         """Returns operator which acts on prior or posterior samples"""
@@ -587,12 +587,12 @@ class CorrelatedFieldMaker:
             return self.average_fluctuation(0)
         q = 1.
         for j in range(len(self._a)):
-            fl = self._a[j].fluctuation_amplitude*self._azm.one_over()
+            fl = self._a[j].fluctuation_amplitude*self._azm.ptw("reciprocal")
             if j == space:
                 q = q*fl**2
             else:
                 q = q*(Adder(full(fl.target, 1.)) @ fl**2)
-        return q.sqrt()*self._azm
+        return q.ptw("sqrt")*self._azm
 
     def average_fluctuation(self, space):
         """Returns operator which acts on prior or posterior samples"""

nifty6/library/dynamic_operator.py

@@ -97,9 +97,9 @@ def _make_dynamic_operator(target, harmonic_padding, sm_s0, sm_x0, cone, keys, c
     m = CentralPadd.adjoint(FFTB(Sm(m)))
     ops['smoothed_dynamics'] = m
 
-    m = -m.log()
+    m = -m.ptw("log")
     if not minimum_phase:
-        m = m.exp()
+        m = m.ptw("exp")
     if causal or minimum_phase:
         m = Real.adjoint(FFT.inverse(Realizer(FFT.target).adjoint(m)))
         kernel = makeOp(
@@ -114,19 +114,19 @@ def _make_dynamic_operator(target, harmonic_padding, sm_s0, sm_x0, cone, keys, c
         c = FieldAdapter(UnstructuredDomain(len(sigc)), keys[1])
         c = makeOp(Field(c.target, np.array(sigc)))(c)
 
-        lightspeed = ScalingOperator(c.target, -0.5)(c).exp()
+        lightspeed = ScalingOperator(c.target, -0.5)(c).ptw("exp")
         scaling = np.array(m.target[0].distances[1:])/m.target[0].distances[0]
         scaling = DiagonalOperator(Field(c.target, scaling))
         ops['lightspeed'] = scaling(lightspeed)
 
-        c = LightConeOperator(c.target, m.target, quant) @ c.exp()
+        c = LightConeOperator(c.target, m.target, quant) @ c.ptw("exp")
         ops['light_cone'] = c
         m = c*m
 
     if causal or minimum_phase:
         m = FFT(Real(m))
     if minimum_phase:
-        m = m.exp()
+        m = m.ptw("exp")
     return m, ops
 
 

nifty6/library/special_distributions.py

@@ -120,7 +120,7 @@ def InverseGammaOperator(domain, alpha, q, delta=1e-2):
         Distance between sampling points for linear interpolation.
     """
     op = _InterpolationOperator(domain, lambda x: invgamma.ppf(norm._cdf(x), float(alpha)),
-                                -8.2, 8.2, delta, lambda x: x.log(), lambda x: x.exp())
+                                -8.2, 8.2, delta, lambda x: x.ptw("log"), lambda x: x.ptw("exp"))
     if np.isscalar(q):
         return op.scale(q)
     return makeOp(q) @ op

nifty6/linearization.py

@@ -166,7 +166,7 @@ class Linearization(object):
         return self.__mul__(1./other)
 
     def __rtruediv__(self, other):
-        return self.one_over().__mul__(other)
+        return self.reciprocal().__mul__(other)
 
     def __pow__(self, power):
         if not np.isscalar(power):
@@ -282,9 +282,10 @@ class Linearization(object):
             self._val.integrate(spaces),
             ContractionOperator(self._jac.target, spaces, 1)(self._jac))
 
-    def exp(self):
-        tmp = self._val.exp()
-        return self.new(tmp, makeOp(tmp)(self._jac))
+    def ptw(self, op):
+        from .pointwise import ptw_dict
+        t1, t2 = self._val.ptw(op, True)
+        return self.new(t1, makeOp(t2)(self._jac))
 
     def clip(self, min=None, max=None):
         tmp = self._val.clip(min, max)
@@ -298,90 +299,6 @@ class Linearization(object):
             tmp2 = makeOp(1. - (tmp == min) - (tmp == max))
         return self.new(tmp, tmp2(self._jac))
 
-    def sqrt(self):
-        tmp = self._val.sqrt()
-        return self.new(tmp, makeOp(0.5/tmp)(self._jac))
-
-    def sin(self):
-        tmp = self._val.sin()
-        tmp2 = self._val.cos()
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def cos(self):
-        tmp = self._val.cos()
-        tmp2 = - self._val.sin()
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def tan(self):
-        tmp = self._val.tan()
-        tmp2 = 1./(self._val.cos()**2)
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def sinc(self):
-        tmp = self._val.sinc()
-        tmp2 = ((np.pi*self._val).cos()-tmp)/self._val
-        ind = self._val.val == 0
-        loc = tmp2.val_rw()
-        loc[ind] = 0
-        tmp2 = Field(tmp.domain, loc)
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def log(self):
-        tmp = self._val.log()
-        return self.new(tmp, makeOp(1./self._val)(self._jac))
-
-    def log10(self):
-        tmp = self._val.log10()
-        tmp2 = 1. / (self._val * np.log(10))
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def log1p(self):
-        tmp = self._val.log1p()
-        tmp2 = 1. / (1. + self._val)
-        return self.new(tmp, makeOp(tmp2)(self.jac))
-
-    def expm1(self):
-        tmp = self._val.expm1()
-        tmp2 = self._val.exp()
-        return self.new(tmp, makeOp(tmp2)(self.jac))
-
-    def sinh(self):
-        tmp = self._val.sinh()
-        tmp2 = self._val.cosh()
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def cosh(self):
-        tmp = self._val.cosh()
-        tmp2 = self._val.sinh()
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def tanh(self):
-        tmp = self._val.tanh()
-        return self.new(tmp, makeOp(1.-tmp**2)(self._jac))
-
-    def sigmoid(self):
-        tmp = self._val.tanh()
-        tmp2 = 0.5*(1.+tmp)
-        return self.new(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
-
-    def absolute(self):
-        if utilities.iscomplextype(self._val.dtype):
-            raise TypeError("Argument must not be complex")
-        tmp = self._val.absolute()
-        tmp2 = self._val.sign()
-
-        ind = self._val.val == 0
-        loc = tmp2.val_rw().astype(float)
-        loc[ind] = np.nan
-        tmp2 = Field(tmp.domain, loc)
-
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
-    def one_over(self):
-        tmp = 1./self._val
-        tmp2 = - tmp/self._val
-        return self.new(tmp, makeOp(tmp2)(self._jac))
-
     def add_metric(self, metric):
         return self.new(self._val, self._jac, metric)
 

nifty6/multi_field.py

@@ -310,8 +310,12 @@ class MultiField(object):
                 res[key] = -val if neg else val
         return MultiField.from_dict(res)
 
-    def one_over(self):
-        return 1/self
+    def ptw(self, op, with_deriv=False):
+        tmp = tuple(val.ptw(op, with_deriv) for val in self.values())
+        if with_deriv:
+            return (MultiField(self.domain, tuple(v[0] for v in tmp)),
+                    MultiField(self.domain, tuple(v[1] for v in tmp)))
+        return MultiField(self.domain, tmp)
 
     def _binary_op(self, other, op):
         f = getattr(Field, op)
@@ -347,14 +351,3 @@ for op in ["__iadd__", "__isub__", "__imul__", "__idiv__",
                 "In-place operations are deliberately not supported")
         return func2
     setattr(MultiField, op, func(op))
-
-
-for f in ["sqrt", "exp", "log", "sin", "cos", "tan", "sinh", "cosh", "tanh",
-          "absolute", "sinc", "sign", "log10", "log1p", "expm1"]:
-    def func(f):
-        def func2(self):
-            fu = getattr(Field, f)
-            return MultiField(self.domain,
-                              tuple(fu(val) for val in self.values()))
-        return func2
-    setattr(MultiField, f, func(f))

nifty6/operators/energy_operators.py

@@ -127,7 +127,7 @@ class VariableCovarianceGaussianEnergy(EnergyOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = 0.5*(x[self._r].vdot(x[self._r]*x[self._icov]).real - x[self._icov].log().sum())
+        res = 0.5*(x[self._r].vdot(x[self._r]*x[self._icov]).real - x[self._icov].ptw("log").sum())
         if not isinstance(x, Linearization) or not x.want_metric:
             return res
         mf = {self._r: x.val[self._icov], self._icov: .5*x.val[self._icov]**(-2)}
@@ -229,7 +229,7 @@ class PoissonianEnergy(EnergyOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = x.sum() - x.log().vdot(self._d)
+        res = x.sum() - x.ptw("log").vdot(self._d)
         if not isinstance(x, Linearization) or not x.want_metric:
             return res
         return res.add_metric(makeOp(1./x.val))
@@ -269,7 +269,7 @@ class InverseGammaLikelihood(EnergyOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = x.log().vdot(self._alphap1) + x.one_over().vdot(self._beta)
+        res = x.ptw("log").vdot(self._alphap1) + x.ptw("reciprocal").vdot(self._beta)
         if not isinstance(x, Linearization) or not x.want_metric:
             return res
         return res.add_metric(makeOp(self._alphap1/(x.val**2)))
@@ -298,7 +298,7 @@ class StudentTEnergy(EnergyOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = ((self._theta+1)/2)*(x**2/self._theta).log1p().sum()
+        res = ((self._theta+1)/2)*(x**2/self._theta).ptw("log1p").sum()
         if not isinstance(x, Linearization) or not x.want_metric:
             return res
         met = ScalingOperator(self.domain, (self._theta+1) / (self._theta+3))
@@ -332,7 +332,7 @@ class BernoulliEnergy(EnergyOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = -x.log().vdot(self._d) + (1.-x).log().vdot(self._d-1.)
+        res = -x.ptw("log").vdot(self._d) + (1.-x).ptw("log").vdot(self._d-1.)
         if not isinstance(x, Linearization) or not x.want_metric:
             return res
         return res.add_metric(makeOp(1./(x.val*(1. - x.val))))

nifty6/operators/operator.py

@@ -222,15 +222,8 @@ class Operator(metaclass=NiftyMeta):
     def _simplify_for_constant_input_nontrivial(self, c_inp):
         return None, self
 
-
-for f in ["sqrt", "exp", "log", "sin", "cos", "tan", "sinh", "cosh", "tanh",
-          "sinc", "sigmoid", "absolute", "one_over", "log10", "log1p", "expm1"]:
-    def func(f):
-        def func2(self):
-            fa = _FunctionApplier(self.target, f)
-            return _OpChain.make((fa, self))
-        return func2
-    setattr(Operator, f, func(f))
+    def ptw(self, op):
+        return _OpChain.make((_FunctionApplier(self.target, op), self))
 
 
 class _ConstCollector(object):
@@ -301,7 +294,7 @@ class _FunctionApplier(Operator):
 
     def apply(self, x):
         self._check_input(x)
-        return getattr(x, self._funcname)()
+        return x.ptw(self._funcname)
 
 
 class _Clipper(Operator):

nifty6/sugar.py

@@ -37,10 +37,7 @@ from .plot import Plot
 __all__ = ['PS_field', 'power_analyze', 'create_power_operator',
            'create_harmonic_smoothing_operator', 'from_random',
            'full', 'makeField',
-           'makeDomain', 'sqrt', 'exp', 'log', 'tanh', 'sigmoid',
-           'sin', 'cos', 'tan', 'sinh', 'cosh', 'log10',
-           'absolute', 'one_over', 'clip', 'sinc', "log1p", "expm1",
-           'conjugate', 'get_signal_variance', 'makeOp', 'domain_union',
+           'makeDomain', 'get_signal_variance', 'makeOp', 'domain_union',
            'get_default_codomain', 'single_plot', 'exec_time',
            'calculate_position']
 
@@ -366,26 +363,6 @@ def domain_union(domains):
     return MultiDomain.union(domains)
 
 
-# Arithmetic functions working on Fields
-
-
-_current_module = sys.modules[__name__]
-
-for f in ["sqrt", "exp", "log", "log10", "tanh", "sigmoid",
-          "conjugate", 'sin', 'cos', 'tan', 'sinh', 'cosh',
-          'absolute', 'one_over', 'sinc', 'log1p', 'expm1']:
-    def func(f):
-        def func2(x):
-            from .linearization import Linearization
-            from .operators.operator import Operator
-            if isinstance(x, (Field, MultiField, Linearization, Operator)):
-                return getattr(x, f)()
-            else:
-                return getattr(np, f)(x)
-        return func2
-    setattr(_current_module, f, func(f))
-
-
 def clip(a, a_min=None, a_max=None):
     return a.clip(a_min, a_max)
 

test/test_energy_gradients.py

@@ -77,7 +77,7 @@ def test_studentt(field):
 
 
 def test_hamiltonian_and_KL(field):
-    field = field.exp()
+    field = field.ptw("exp")
     space = field.domain
     lh = ift.GaussianEnergy(domain=space)
     hamiltonian = ift.StandardHamiltonian(lh)
@@ -91,7 +91,7 @@ def test_hamiltonian_and_KL(field):
 def test_variablecovariancegaussian(field):
     if isinstance(field.domain, ift.MultiDomain):
         return
-    dc = {'a': field, 'b': field.exp()}
+    dc = {'a': field, 'b': field.ptw("exp")}
     mf = ift.MultiField.from_dict(dc)
     energy = ift.VariableCovarianceGaussianEnergy(field.domain, 'a', 'b')
     ift.extra.check_jacobian_consistency(energy, mf, tol=1e-6)
@@ -101,7 +101,7 @@ def test_variablecovariancegaussian(field):
 def test_inverse_gamma(field):
     if isinstance(field.domain, ift.MultiDomain):
         return
-    field = field.exp()
+    field = field.ptw("exp")
     space = field.domain
     d = ift.random.current_rng().normal(10, size=space.shape)**2
     d = ift.Field(space, d)
@@ -112,7 +112,7 @@ def test_inverse_gamma(field):
 def testPoissonian(field):
     if isinstance(field.domain, ift.MultiDomain):
         return
-    field = field.exp()
+    field = field.ptw("exp")
     space = field.domain
     d = ift.random.current_rng().poisson(120, size=space.shape)
     d = ift.Field(space, d)
@@ -123,7 +123,7 @@ def testPoissonian(field):
 def test_bernoulli(field):
     if isinstance(field.domain, ift.MultiDomain):
         return
-    field = field.sigmoid()
+    field = field.ptw("sigmoid")
     space = field.domain
     d = ift.random.current_rng().binomial(1, 0.1, size=space.shape)
     d = ift.Field(space, d)

test/test_field.py

@@ -198,7 +198,7 @@ def test_trivialities():
     assert_equal(f1.val, f1.real.val)
     assert_equal(f1.val, (+f1).val)
     f1 = ift.Field.full(s1, 27. + 3j)
-    assert_equal(f1.one_over().val, (1./f1).val)
+    assert_equal(f1.ptw("reciprocal").val, (1./f1).val)
     assert_equal(f1.real.val, 27.)
     assert_equal(f1.imag.val, 3.)
     assert_equal(f1.s_sum(), f1.sum(0).val)
@@ -336,7 +336,7 @@ def test_emptydomain():
 def test_funcs(num, dom, func):
     num = 5
     f = ift.Field.full(dom, num)
-    res = getattr(f, func)()
+    res = f.ptw(func)
     res2 = getattr(np, func)(num)
     assert_allclose(res.val, res2)
 

test/test_gaussian_energy.py

@@ -51,7 +51,7 @@ def test_gaussian_energy(space, nonlinearity, noise, seed):
             return 1/(1 + k**2)**dim
 
         pspec = ift.PS_field(pspace, pspec)
-        A = Dist(ift.sqrt(pspec))
+        A = Dist(pspec.ptw("sqrt"))
         N = ift.ScalingOperator(space, noise)
         n = N.draw_sample()
         R = ift.ScalingOperator(space, 10.)
@@ -61,7 +61,7 @@ def test_gaussian_energy(space, nonlinearity, noise, seed):
                 return R @ ht @ ift.makeOp(A)
             else:
                 tmp = ht @ ift.makeOp(A)
-                nonlin = getattr(tmp, nonlinearity)()
+                nonlin = tmp.ptw(nonlinearity)
                 return R @ nonlin
 
         d = d_model()(xi0) + n

test/test_linearization.py

@@ -43,19 +43,19 @@ def test_special_gradients():
     jt(var.clip(-1, 0), np.zeros_like(s))
 
     assert_allclose(
-        _lin2grad(ift.Linearization.make_var(0*f).sinc()), np.zeros(s.shape))
-    assert_(np.isnan(_lin2grad(ift.Linearization.make_var(0*f).absolute())))
+        _lin2grad(ift.Linearization.make_var(0*f).ptw("sinc")), np.zeros(s.shape))
+    assert_(np.isnan(_lin2grad(ift.Linearization.make_var(0*f).ptw("abs"))))
     assert_allclose(
-        _lin2grad(ift.Linearization.make_var(0*f + 10).absolute()),
+        _lin2grad(ift.Linearization.make_var(0*f + 10).ptw("abs")),
         np.ones(s.shape))
     assert_allclose(
-        _lin2grad(ift.Linearization.make_var(0*f - 10).absolute()),
+        _lin2grad(ift.Linearization.make_var(0*f - 10).ptw("abs")),
         -np.ones(s.shape))
 
 
 @pmp('f', [
     'log', 'exp', 'sqrt', 'sin', 'cos', 'tan', 'sinc', 'sinh', 'cosh', 'tanh',
-    'absolute', 'one_over', 'sigmoid', 'log10', 'log1p', "expm1"
+    'absolute', 'reciprocal', 'sigmoid', 'log10', 'log1p', "expm1"
 ])
 def test_actual_gradients(f):
     dom = ift.UnstructuredDomain((1,))
@@ -63,8 +63,8 @@ def test_actual_gradients(f):
     eps = 1e-8
     var0 = ift.Linearization.make_var(fld)
     var1 = ift.Linearization.make_var(fld + eps)
-    f0 = getattr(var0, f)().val.val
-    f1 = getattr(var1, f)().val.val
+    f0 = var0.ptw(f).val.val
+    f1 = var1.ptw(f).val.val
     df0 = (f1 - f0)/eps
-    df1 = _lin2grad(getattr(var0, f)())
+    df1 = _lin2grad(var0.ptw(f))
     assert_allclose(df0, df1, rtol=100*eps)

test/test_multi_field.py

@@ -33,7 +33,7 @@ def test_vdot():
 def test_func():
     f1 = ift.from_random("normal", domain=dom, dtype=np.complex128)
     assert_allclose(
-        ift.log(ift.exp((f1)))["d1"].val, f1["d1"].val)
+        f1.ptw("exp").ptw("log")["d1"].val, f1["d1"].val)
 
 
 def test_multifield_field_consistency():

test/test_operators/test_convolution_operators.py

@@ -46,7 +46,7 @@ def test_gaussian_smoothing():