diff --git a/demos/bernoulli_demo.py b/demos/bernoulli_demo.py
index d8eb410663422f0b7954c449fdabaa06e766bf32..76fec1a116c4f7d804ff108e6935645d6e4e06ef 100644
--- a/demos/bernoulli_demo.py
+++ b/demos/bernoulli_demo.py
@@ -52,7 +52,7 @@ if __name__ == '__main__':
A = ift.create_power_operator(harmonic_space, sqrtpspec)
# Set up a sky operator and instrumental response
- sky = HT(A).ptw("sigmoid")
+ sky = ift.sigmoid(HT(A))
GR = ift.GeometryRemover(position_space)
R = GR
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index 49168d45bf8da023a2fbadab88839b96e9695351..7856049d9efac5bb6ca51ce3745be05f0b5a446a 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -80,7 +80,7 @@ if __name__ == '__main__':
A = pd(a)
# Define sky operator
- sky = HT(ift.makeOp(A)).ptw("exp")
+ sky = ift.exp(HT(ift.makeOp(A)))
M = ift.DiagonalOperator(exposure)
GR = ift.GeometryRemover(position_space)
diff --git a/demos/getting_started_3.py b/demos/getting_started_3.py
index b4da83a885fd7fd14d46c76d10648bfcfcda28c3..d83bcf14fa9554291bb1855ca28f78fbbed007f6 100644
--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -85,7 +85,7 @@ if __name__ == '__main__':
A = cfmaker.amplitude
# Apply a nonlinearity
- signal = correlated_field.ptw("sigmoid")
+ signal = ift.sigmoid(correlated_field)
# Build the line-of-sight response and define signal response
LOS_starts, LOS_ends = random_los(100) if mode == 0 else radial_los(100)
@@ -149,7 +149,7 @@ if __name__ == '__main__':
filename_res = filename.format("results")
plot = ift.Plot()
plot.add(sc.mean, title="Posterior Mean")
- plot.add(sc.var.ptw("sqrt"), title="Posterior Standard Deviation")
+ plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
powers = [A.force(s + KL.position) for s in KL.samples]
plot.add(
diff --git a/demos/getting_started_mf.py b/demos/getting_started_mf.py
index 992b2b1e45cb700d0943a1895d27433f3b64b452..e4ee04cf6408a8b4308f8b3fa227d7bf4937b27c 100644
--- a/demos/getting_started_mf.py
+++ b/demos/getting_started_mf.py
@@ -84,7 +84,7 @@ if __name__ == '__main__':
DC = SingleDomain(correlated_field.target, position_space)
## Apply a nonlinearity
- signal = DC @ correlated_field.ptw("sigmoid")
+ signal = DC @ ift.sigmoid(correlated_field)
# Build the line-of-sight response and define signal response
LOS_starts, LOS_ends = random_los(100) if mode == 0 else radial_los(100)
@@ -170,7 +170,7 @@ if __name__ == '__main__':
filename_res = filename.format("results")
plot = ift.Plot()
plot.add(sc.mean, title="Posterior Mean")
- plot.add(sc.var.ptw("sqrt"), title="Posterior Standard Deviation")
+ plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
powers1 = [A1.force(s + KL.position) for s in KL.samples]
powers2 = [A2.force(s + KL.position) for s in KL.samples]
diff --git a/nifty6/field.py b/nifty6/field.py
index decf826eecb7b197a406b35627067d92de9f05a3..8d4d31970afa822cda43dda44b28e005b5c6e581 100644
--- a/nifty6/field.py
+++ b/nifty6/field.py
@@ -686,9 +686,6 @@ class Field(Operator):
def flexible_addsub(self, other, neg):
return self-other if neg else self+other
- def clip(self, a_min=None, a_max=None):
- return self.ptw("clip", a_min, a_max)
-
def _binary_op(self, other, op):
# if other is a field, make sure that the domains match
f = getattr(self._val, op)
@@ -700,20 +697,24 @@ class Field(Operator):
return Field(self._domain, f(other))
return NotImplemented
- def ptw(self, op, *args, **kwargs):
- from .pointwise import ptw_dict
+ def _prep_args(self, args, kwargs):
+ for arg in args + tuple(kwargs.values()):
+ if not (arg is None or np.isscalar(arg) or arg.jac is None):
+ raise TypeError("bad argument")
argstmp = tuple(arg if arg is None or np.isscalar(arg) else arg._val
for arg in args)
kwargstmp = {key: val if val is None or np.isscalar(val) else val._val
for key, val in kwargs.items()}
+ return argstmp, kwargstmp
+
+ def ptw(self, op, *args, **kwargs):
+ from .pointwise import ptw_dict
+ argstmp, kwargstmp = self._prep_args(args, kwargs)
return Field(self._domain, ptw_dict[op][0](self._val, *argstmp, **kwargstmp))
def ptw_with_deriv(self, op, *args, **kwargs):
from .pointwise import ptw_dict
- argstmp = tuple(arg if arg is None or np.isscalar(arg) else arg._val
- for arg in args)
- kwargstmp = {key: val if val is None or np.isscalar(val) else val._val
- for key, val in kwargs.items()}
+ argstmp, kwargstmp = self._prep_args(args, kwargs)
tmp = ptw_dict[op][1](self._val, *argstmp, **kwargstmp)
return (Field(self._domain, tmp[0]), Field(self._domain, tmp[1]))
diff --git a/nifty6/library/correlated_fields.py b/nifty6/library/correlated_fields.py
index d010e2999c1fdedb278191843826f8dcccba6453..c5156c3e1836bccc3b59146327e5eaea694dc1f8 100644
--- a/nifty6/library/correlated_fields.py
+++ b/nifty6/library/correlated_fields.py
@@ -225,7 +225,7 @@ class _Normalization(Operator):
def apply(self, x):
self._check_input(x)
amp = x.ptw("exp")
- spec = amp*amp
+ spec = amp**2
# 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
diff --git a/nifty6/linearization.py b/nifty6/linearization.py
index bd577f6deb9a4293088a42feff4a3db3757baf62..9b84b86e9637217f859b82611272f4db9723d186 100644
--- a/nifty6/linearization.py
+++ b/nifty6/linearization.py
@@ -294,15 +294,6 @@ class Linearization(Operator):
t1, t2 = self._fld.ptw_with_deriv(op, *args, **kwargs)
return self.new(t1, makeOp(t2)(self._jac))
- def clip(self, a_min=None, a_max=None):
- if a_min is None and a_max is None:
- return self
- if not (a_min is None or np.isscalar(a_min) or a_min.jac is None):
- return NotImplemented
- if not (a_max is None or np.isscalar(a_max) or a_max.jac is None):
- return NotImplemented
- return self.ptw("clip", a_min, a_max)
-
def add_metric(self, metric):
return self.new(self._fld, self._jac, metric)
diff --git a/nifty6/multi_field.py b/nifty6/multi_field.py
index aca5f78a53e8653e397f19340936e93707ab7544..a05bd644ab88c84107bbf254bc7602464d0a0412 100644
--- a/nifty6/multi_field.py
+++ b/nifty6/multi_field.py
@@ -314,25 +314,27 @@ class MultiField(Operator):
res[key] = -val if neg else val
return MultiField.from_dict(res)
+ def _prep_args(self, args, kwargs, i):
+ for arg in args + tuple(kwargs.values()):
+ if not (arg is None or np.isscalar(arg) or arg.jac is None):
+ raise TypeError("bad argument")
+ argstmp = tuple(arg if arg is None or np.isscalar(arg) else arg._val[i]
+ for arg in args)
+ kwargstmp = {key: val if val is None or np.isscalar(val) else val._val[i]
+ for key, val in kwargs.items()}
+ return argstmp, kwargstmp
+
def ptw(self, op, *args, **kwargs):
-# _check_args(args, kwargs)
tmp = []
for i in range(len(self._val)):
- argstmp = tuple(arg if arg is None or np.isscalar(arg) else arg._val[i]
- for arg in args)
- kwargstmp = {key: val if val is None or np.isscalar(val) else val._val[i]
- for key, val in kwargs.items()}
+ argstmp, kwargstmp = self._prep_args(args, kwargs, i)
tmp.append(self._val[i].ptw(op, *argstmp, **kwargstmp))
return MultiField(self.domain, tuple(tmp))
def ptw_with_deriv(self, op, *args, **kwargs):
-# _check_args(args, kwargs)
tmp = []
for i in range(len(self._val)):
- argstmp = tuple(arg if arg is None or np.isscalar(arg) else arg._val[i]
- for arg in args)
- kwargstmp = {key: val if val is None or np.isscalar(val) else val._val[i]
- for key, val in kwargs.items()}
+ argstmp, kwargstmp = self._prep_args(args, kwargs, i)
tmp.append(self._val[i].ptw_with_deriv(op, *argstmp, **kwargstmp))
return (MultiField(self.domain, tuple(v[0] for v in tmp)),
MultiField(self.domain, tuple(v[1] for v in tmp)))
diff --git a/nifty6/operators/operator.py b/nifty6/operators/operator.py
index e0808671af85a524ce61c71b57c4659dfd69a302..fa4e63501588df64471426108470cebd70926a80 100644
--- a/nifty6/operators/operator.py
+++ b/nifty6/operators/operator.py
@@ -18,6 +18,7 @@
import numpy as np
from ..utilities import NiftyMeta, indent
+from .. import pointwise
class Operator(metaclass=NiftyMeta):
@@ -221,15 +222,6 @@ class Operator(metaclass=NiftyMeta):
return NotImplemented
return self.ptw("power", power)
- def clip(self, a_min=None, a_max=None):
- if a_min is None and a_max is None:
- return self
- if not (a_min is None or np.isscalar(a_min) or a_min.jac is None):
- return NotImplemented
- if not (a_max is None or np.isscalar(a_max) or a_max.jac is None):
- return NotImplemented
- return self.ptw("clip", a_min, a_max)
-
def apply(self, x):
"""Applies the operator to a Field or MultiField.
@@ -292,6 +284,14 @@ class Operator(metaclass=NiftyMeta):
return _OpChain.make((_FunctionApplier(self.target, op, *args, **kwargs), self))
+for f in pointwise.ptw_dict.keys():
+ def func(f):
+ def func2(self, *args, **kwargs):
+ return self.ptw(f, *args, **kwargs)
+ return func2
+ setattr(Operator, f, func(f))
+
+
class _ConstCollector(object):
def __init__(self):
self._const = None
diff --git a/nifty6/pointwise.py b/nifty6/pointwise.py
index 06f8bedd15c0b978a8195d1bfaac406c890134ac..198518a61bd510f7eb851ff1677c091f3f8b68f9 100644
--- a/nifty6/pointwise.py
+++ b/nifty6/pointwise.py
@@ -67,7 +67,7 @@ def _power_helper(v, expo):
return (np.power(v, expo), expo*np.power(v, expo-1))
-def _clip_helper(v, a_min=None, a_max=None):
+def _clip_helper(v, a_min, a_max):
if np.issubdtype(v.dtype, np.complexfloating):
raise TypeError("Argument must not be complex")
tmp = np.clip(v, a_min, a_max)
diff --git a/nifty6/sugar.py b/nifty6/sugar.py
index 4dd40296bde74032ebf6921b70be4b3fc7e66f39..c1afe9de2737d68709eb1004080675db646d5076 100644
--- a/nifty6/sugar.py
+++ b/nifty6/sugar.py
@@ -33,13 +33,15 @@ from .operators.distributors import PowerDistributor
from .operators.operator import Operator
from .operators.scaling_operator import ScalingOperator
from .plot import Plot
+from . import pointwise
+
__all__ = ['PS_field', 'power_analyze', 'create_power_operator',
'create_harmonic_smoothing_operator', 'from_random',
'full', 'makeField',
'makeDomain', 'get_signal_variance', 'makeOp', 'domain_union',
'get_default_codomain', 'single_plot', 'exec_time',
- 'calculate_position']
+ 'calculate_position'] + list(pointwise.ptw_dict.keys())
def PS_field(pspace, func):
@@ -341,7 +343,7 @@ def makeOp(input, dom=None):
if input is None:
return None
if np.isscalar(input):
- if not isinstance(dom, (DomaiTuple, MultiDomain)):
+ if not isinstance(dom, (DomainTuple, MultiDomain)):
raise TypeError("need proper `dom` argument")
return SalingOperator(dom, input)
if dom is not None:
@@ -373,8 +375,16 @@ def domain_union(domains):
return MultiDomain.union(domains)
-def clip(a, a_min=None, a_max=None):
- return a.clip(a_min, a_max)
+# Pointwise functions
+
+_current_module = sys.modules[__name__]
+
+for f in pointwise.ptw_dict.keys():
+ def func(f):
+ def func2(x, *args, **kwargs):
+ return x.ptw(f, *args, **kwargs)
+ return func2
+ setattr(_current_module, f, func(f))
def get_default_codomain(domainoid, space=None):
diff --git a/test/test_field.py b/test/test_field.py
index 91566d22f37472fc066ba21144bf227f462ca113..6e154cb1994652ae2924ad590502ab53e11f4a23 100644
--- a/test/test_field.py
+++ b/test/test_field.py
@@ -193,8 +193,8 @@ def test_empty_domain():
def test_trivialities():
s1 = ift.RGSpace((10,))
f1 = ift.Field.full(s1, 27)
- assert_equal(f1.clip(a_min=29).val, 29.)
- assert_equal(f1.clip(a_max=25).val, 25.)
+ assert_equal(f1.clip(a_min=29, a_max=50).val, 29.)
+ assert_equal(f1.clip(a_min=0, a_max=25).val, 25.)
assert_equal(f1.val, f1.real.val)
assert_equal(f1.val, (+f1).val)
f1 = ift.Field.full(s1, 27. + 3j)