Merge branch 'static_restructure' into 'NIFTy_4'

Static restructure

See merge request ift/NIFTy!259

demos/critical_filtering.py

@@ -69,8 +69,8 @@ if __name__ == "__main__":
     # Creating the mock data
     d = noiseless_data + n
 
-    m0 = ift.Field.full(h_space, 1e-7)
-    t0 = ift.Field.full(p_space, -4.)
+    m0 = ift.full(h_space, 1e-7)
+    t0 = ift.full(p_space, -4.)
     power0 = Distributor.times(ift.exp(0.5 * t0))
 
     plotdict = {"colormap": "Planck-like"}

demos/krylov_sampling.py

@@ -67,8 +67,8 @@ plt.legend()
 plt.savefig('Krylov_samples_residuals.png')
 plt.close()
 
-D_hat_old = ift.Field.zeros(x_space).to_global_data()
-D_hat_new = ift.Field.zeros(x_space).to_global_data()
+D_hat_old = ift.full(x_space, 0.).to_global_data()
+D_hat_new = ift.full(x_space, 0.).to_global_data()
 for i in range(N_samps):
     D_hat_old += sky(samps_old[i]).to_global_data()**2
     D_hat_new += sky(samps[i]).to_global_data()**2

demos/nonlinear_critical_filter.py

@@ -69,8 +69,8 @@ if __name__ == "__main__":
     # Creating the mock data
     d = noiseless_data + n
 
-    m0 = ift.Field.full(h_space, 1e-7)
-    t0 = ift.Field.full(p_space, -4.)
+    m0 = ift.full(h_space, 1e-7)
+    t0 = ift.full(p_space, -4.)
     power0 = Distributor.times(ift.exp(0.5 * t0))
 
     IC1 = ift.GradientNormController(name="IC1", iteration_limit=100,

demos/nonlinear_wiener_filter.py

@@ -36,7 +36,7 @@ if __name__ == "__main__":
     d_space = R.target
 
     p_op = ift.create_power_operator(h_space, p_spec)
-    power = ift.sqrt(p_op(ift.Field.full(h_space, 1.)))
+    power = ift.sqrt(p_op(ift.full(h_space, 1.)))
 
     # Creating the mock data
     true_sky = nonlinearity(HT(power*sh))
@@ -57,7 +57,7 @@ if __name__ == "__main__":
     inverter = ift.ConjugateGradient(controller=ICI)
 
     # initial guess
-    m = ift.Field.full(h_space, 1e-7)
+    m = ift.full(h_space, 1e-7)
     map_energy = ift.library.NonlinearWienerFilterEnergy(
         m, d, R, nonlinearity, HT, power, N, S, inverter=inverter)
 

demos/poisson_demo.py

@@ -113,7 +113,7 @@ if __name__ == "__main__":
         d_domain, np.random.poisson(lam.local_data).astype(np.float64))
 
     # initial guess
-    psi0 = ift.Field.full(h_domain, 1e-7)
+    psi0 = ift.full(h_domain, 1e-7)
     energy = ift.library.PoissonEnergy(psi0, data, R0, nonlin, HT, Phi_h,
                                        inverter)
     IC1 = ift.GradientNormController(name="IC1", iteration_limit=200,

demos/wiener_filter_via_hamiltonian.py

@@ -50,7 +50,7 @@ if __name__ == "__main__":
     inverter = ift.ConjugateGradient(controller=ctrl)
     controller = ift.GradientNormController(name="min", tol_abs_gradnorm=0.1)
     minimizer = ift.RelaxedNewton(controller=controller)
-    m0 = ift.Field.zeros(h_space)
+    m0 = ift.full(h_space, 0.)
 
     # Initialize Wiener filter energy
     energy = ift.library.WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S,

nifty4/__init__.py

@@ -8,7 +8,7 @@ from .domain_tuple import DomainTuple
 
 from .operators import *
 
-from .field import Field, sqrt, exp, log
+from .field import Field
 
 from .probing.utils import probe_with_posterior_samples, probe_diagonal, \
     StatCalculator

nifty4/data_objects/distributed_do.py

@@ -20,6 +20,7 @@ import numpy as np
 from .random import Random
 from mpi4py import MPI
 import sys
+from functools import reduce
 
 _comm = MPI.COMM_WORLD
 ntask = _comm.Get_size()
@@ -145,20 +146,29 @@ class data_object(object):
     def sum(self, axis=None):
         return self._contraction_helper("sum", MPI.SUM, axis)
 
+    def prod(self, axis=None):
+        return self._contraction_helper("prod", MPI.PROD, axis)
+
     def min(self, axis=None):
         return self._contraction_helper("min", MPI.MIN, axis)
 
     def max(self, axis=None):
         return self._contraction_helper("max", MPI.MAX, axis)
 
-    def mean(self):
-        return self.sum()/self.size
+    def mean(self, axis=None):
+        if axis is None:
+            sz = self.size
+        else:
+            sz = reduce(lambda x, y: x*y, [self.shape[i] for i in axis])
+        return self.sum(axis)/sz
 
-    def std(self):
-        return np.sqrt(self.var())
+    def std(self, axis=None):
+        return np.sqrt(self.var(axis))
 
     # FIXME: to be improved!
-    def var(self):
+    def var(self, axis=None):
+        if axis is not None and len(axis) != len(self.shape):
+            raise ValueError("functionality not yet supported")
         return (abs(self-self.mean())**2).mean()
 
     def _binary_helper(self, other, op):

nifty4/domain_tuple.py

@@ -34,7 +34,9 @@ class DomainTuple(object):
     """
     _tupleCache = {}
 
-    def __init__(self, domain):
+    def __init__(self, domain, _callingfrommake=False):
+        if not _callingfrommake:
+            raise NotImplementedError
         self._dom = self._parse_domain(domain)
         self._axtuple = self._get_axes_tuple()
         shape_tuple = tuple(sp.shape for sp in self._dom)
@@ -72,7 +74,7 @@ class DomainTuple(object):
         obj = DomainTuple._tupleCache.get(domain)
         if obj is not None:
             return obj
-        obj = DomainTuple(domain)
+        obj = DomainTuple(domain, _callingfrommake=True)
         DomainTuple._tupleCache[domain] = obj
         return obj
 

nifty4/domains/domain.py

@@ -23,6 +23,8 @@ from ..utilities import NiftyMetaBase
 class Domain(NiftyMetaBase()):
     """The abstract class repesenting a (structured or unstructured) domain.
     """
+    def __init__(self):
+        self._hash = None
 
     @abc.abstractmethod
     def __repr__(self):
@@ -36,10 +38,12 @@ class Domain(NiftyMetaBase()):
         Only members that are explicitly added to
         :attr:`._needed_for_hash` will be used for hashing.
         """
-        result_hash = 0
-        for key in self._needed_for_hash:
-            result_hash ^= hash(vars(self)[key])
-        return result_hash
+        if self._hash is None:
+            h = 0
+            for key in self._needed_for_hash:
+                h ^= hash(vars(self)[key])
+            self._hash = h
+        return self._hash
 
     def __eq__(self, x):
         """Checks whether two domains are equal.

nifty4/domains/lm_space.py

@@ -19,7 +19,7 @@
 from __future__ import division
 import numpy as np
 from .structured_domain import StructuredDomain
-from ..field import Field, exp
+from ..field import Field
 
 
 class LMSpace(StructuredDomain):
@@ -100,6 +100,8 @@ class LMSpace(StructuredDomain):
         # cf. "All-sky convolution for polarimetry experiments"
         # by Challinor et al.
         # http://arxiv.org/abs/astro-ph/0008228
+        from ..sugar import exp
+
         res = x+1.
         res *= x
         res *= -0.5*sigma*sigma

nifty4/domains/rg_space.py

@@ -21,7 +21,7 @@ from builtins import range
 from functools import reduce
 import numpy as np
 from .structured_domain import StructuredDomain
-from ..field import Field, exp
+from ..field import Field
 from .. import dobj
 
 
@@ -144,6 +144,7 @@ class RGSpace(StructuredDomain):
 
     @staticmethod
     def _kernel(x, sigma):
+        from ..sugar import exp
         tmp = x*x
         tmp *= -2.*np.pi*np.pi*sigma*sigma
         exp(tmp, out=tmp)

nifty4/extra/operator_tests.py

@@ -17,17 +17,26 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 import numpy as np
+from ..sugar import from_random
 from ..field import Field
 
 __all__ = ["consistency_check"]
 
 
+def _assert_allclose(f1, f2, atol, rtol):
+    if isinstance(f1, Field):
+        return np.testing.assert_allclose(f1.local_data, f2.local_data,
+                                          atol=atol, rtol=rtol)
+    for key, val in f1.items():
+        _assert_allclose(val, f2[key], atol=atol, rtol=rtol)
+
+
 def adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol):
     needed_cap = op.TIMES | op.ADJOINT_TIMES
     if (op.capability & needed_cap) != needed_cap:
         return
-    f1 = Field.from_random("normal", op.domain, dtype=domain_dtype).lock()
-    f2 = Field.from_random("normal", op.target, dtype=target_dtype).lock()
+    f1 = from_random("normal", op.domain, dtype=domain_dtype).lock()
+    f2 = from_random("normal", op.target, dtype=target_dtype).lock()
     res1 = f1.vdot(op.adjoint_times(f2).lock())
     res2 = op.times(f1).vdot(f2)
     np.testing.assert_allclose(res1, res2, atol=atol, rtol=rtol)
@@ -37,15 +46,13 @@ def inverse_implementation(op, domain_dtype, target_dtype, atol, rtol):
     needed_cap = op.TIMES | op.INVERSE_TIMES
     if (op.capability & needed_cap) != needed_cap:
         return
-    foo = Field.from_random("normal", op.target, dtype=target_dtype).lock()
+    foo = from_random("normal", op.target, dtype=target_dtype).lock()
     res = op(op.inverse_times(foo).lock())
-    np.testing.assert_allclose(res.to_global_data(), res.to_global_data(),
-                               atol=atol, rtol=rtol)
+    _assert_allclose(res, foo, atol=atol, rtol=rtol)
 
-    foo = Field.from_random("normal", op.domain, dtype=domain_dtype).lock()
+    foo = from_random("normal", op.domain, dtype=domain_dtype).lock()
     res = op.inverse_times(op(foo).lock())
-    np.testing.assert_allclose(res.to_global_data(), foo.to_global_data(),
-                               atol=atol, rtol=rtol)
+    _assert_allclose(res, foo, atol=atol, rtol=rtol)
 
 
 def full_implementation(op, domain_dtype, target_dtype, atol, rtol):

nifty4/field.py

@@ -106,62 +106,10 @@ class Field(object):
             raise TypeError("val must be a scalar")
         return Field(DomainTuple.make(domain), val, dtype)
 
-    @staticmethod
-    def ones(domain, dtype=None):
-        return Field(DomainTuple.make(domain), 1., dtype)
-
-    @staticmethod
-    def zeros(domain, dtype=None):
-        return Field(DomainTuple.make(domain), 0., dtype)
-
     @staticmethod
     def empty(domain, dtype=None):
         return Field(DomainTuple.make(domain), None, dtype)
 
-    @staticmethod
-    def full_like(field, val, dtype=None):
-        """Creates a Field from a template, filled with a constant value.
-
-        Parameters
-        ----------
-        field : Field
-            the template field, from which the domain is inferred
-        val : float/complex/int scalar
-            fill value. Data type of the field is inferred from val.
-
-        Returns
-        -------
-        Field
-            the newly created field
-        """
-        if not isinstance(field, Field):
-            raise TypeError("field must be of Field type")
-        return Field.full(field._domain, val, dtype)
-
-    @staticmethod
-    def zeros_like(field, dtype=None):
-        if not isinstance(field, Field):
-            raise TypeError("field must be of Field type")
-        if dtype is None:
-            dtype = field.dtype
-        return Field.zeros(field._domain, dtype)
-
-    @staticmethod
-    def ones_like(field, dtype=None):
-        if not isinstance(field, Field):
-            raise TypeError("field must be of Field type")
-        if dtype is None:
-            dtype = field.dtype
-        return Field.ones(field._domain, dtype)
-
-    @staticmethod
-    def empty_like(field, dtype=None):
-        if not isinstance(field, Field):
-            raise TypeError("field must be of Field type")
-        if dtype is None:
-            dtype = field.dtype
-        return Field.empty(field._domain, dtype)
-
     @staticmethod
     def from_global_data(domain, arr, sum_up=False):
         """Returns a Field constructed from `domain` and `arr`.
@@ -287,6 +235,7 @@ class Field(object):
             The value to fill the field with.
         """
         self._val.fill(fill_value)
+        return self
 
     def lock(self):
         """Write-protect the data content of `self`.
@@ -370,6 +319,17 @@ class Field(object):
         """
         return Field(val=self, copy=True)
 
+    def empty_copy(self):
+        """ Returns a Field with identical domain and data type, but
+        uninitialized data.
+
+        Returns
+        -------
+        Field
+            A copy of 'self', with uninitialized data.
+        """
+        return Field(self._domain, dtype=self.dtype)
+
     def locked_copy(self):
         """ Returns a read-only version of the Field.
 
@@ -503,8 +463,8 @@ class Field(object):
                               or Field (for partial dot products)
         """
         if not isinstance(x, Field):
-            raise ValueError("The dot-partner must be an instance of " +
-                             "the NIFTy field class")
+            raise TypeError("The dot-partner must be an instance of " +
+                            "the NIFTy field class")
 
         if x._domain != self._domain:
             raise ValueError("Domain mismatch")
@@ -694,7 +654,8 @@ class Field(object):
         if self.scalar_weight(spaces) is not None:
             return self._contraction_helper('mean', spaces)
         # MR FIXME: not very efficient
-        tmp = self.weight(1)
+        # MR FIXME: do we need "spaces" here?
+        tmp = self.weight(1, spaces)
         return tmp.sum(spaces)*(1./tmp.total_volume(spaces))
 
     def var(self, spaces=None):
@@ -717,12 +678,10 @@ class Field(object):
         # MR FIXME: not very efficient or accurate
         m1 = self.mean(spaces)
         if np.issubdtype(self.dtype, np.complexfloating):
-            sq = abs(self)**2
-            m1 = abs(m1)**2
+            sq = abs(self-m1)**2
         else:
-            sq = self**2
-            m1 **= 2
-        return sq.mean(spaces) - m1
+            sq = (self-m1)**2
+        return sq.mean(spaces)
 
     def std(self, spaces=None):
         """Determines the standard deviation over the sub-domains given by
@@ -742,6 +701,7 @@ class Field(object):
             The result of the operation. If it is carried out over the entire
             domain, this is a scalar, otherwise a Field.
         """
+        from .sugar import sqrt
         if self.scalar_weight(spaces) is not None:
             return self._contraction_helper('std', spaces)
         return sqrt(self.var(spaces))
@@ -785,24 +745,3 @@ for op in ["__add__", "__radd__", "__iadd__",
             return NotImplemented
         return func2
     setattr(Field, op, func(op))
-
-
-# Arithmetic functions working on Fields
-
-_current_module = sys.modules[__name__]
-
-for f in ["sqrt", "exp", "log", "tanh", "conjugate"]:
-    def func(f):
-        def func2(x, out=None):
-            fu = getattr(dobj, f)
-            if not isinstance(x, Field):
-                raise TypeError("This function only accepts Field objects.")
-            if out is not None:
-                if not isinstance(out, Field) or x._domain != out._domain:
-                    raise ValueError("Bad 'out' argument")
-                fu(x.val, out=out.val)
-                return out
-            else:
-                return Field(domain=x._domain, val=fu(x.val))
-        return func2
-    setattr(_current_module, f, func(f))

nifty4/library/krylov_sampling.py

@@ -54,7 +54,7 @@ def generate_krylov_samples(D_inv, S, j, N_samps, controller):
     """
     # RL FIXME: make consistent with complex numbers
     j = S.draw_sample(from_inverse=True) if j is None else j
-    energy = QuadraticEnergy(j*0., D_inv, j)
+    energy = QuadraticEnergy(j.empty_copy().fill(0.), D_inv, j)
     y = [S.draw_sample() for _ in range(N_samps)]
 
     status = controller.start(energy)

nifty4/library/noise_energy.py

@@ -16,7 +16,8 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from ..field import Field, exp
+from ..field import Field
+from ..sugar import exp
 from ..minimization.energy import Energy
 from ..operators.diagonal_operator import DiagonalOperator
 import numpy as np

nifty4/library/nonlinear_power_energy.py

@@ -16,7 +16,7 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from .. import exp
+from ..sugar import exp
 from ..minimization.energy import Energy
 from ..operators.smoothness_operator import SmoothnessOperator
 from ..operators.inversion_enabler import InversionEnabler

nifty4/library/nonlinearities.py

@@ -16,7 +16,7 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from ..field import Field, exp, tanh
+from ..sugar import full, exp, tanh
 
 
 class Linear(object):
@@ -24,10 +24,10 @@ class Linear(object):
         return x
 
     def derivative(self, x):
-        return Field.ones_like(x)
+        return full(x.domain, 1.)
 
     def hessian(self, x):
-        return Field.zeros_like(x)
+        return full(x.domain, 0.)
 
 
 class Exponential(object):

nifty4/library/poisson_energy.py

@@ -20,7 +20,7 @@ from ..minimization.energy import Energy
 from ..operators.diagonal_operator import DiagonalOperator
 from ..operators.sandwich_operator import SandwichOperator
 from ..operators.inversion_enabler import InversionEnabler
-from ..field import log
+from ..sugar import log
 
 
 class PoissonEnergy(Energy):

nifty4/logger.py

@@ -16,6 +16,7 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
+
 def _logger_init():
     import logging
     from . import dobj

nifty4/multi/__init__.py

 from .multi_domain import MultiDomain
 from .multi_field import MultiField
+from .block_diagonal_operator import BlockDiagonalOperator
 
-__all__ = ["MultiDomain", "MultiField"]
+__all__ = ["MultiDomain", "MultiField", "BlockDiagonalOperator"]

nifty4/multi/block_diagonal_operator.py

+import numpy as np
+from ..operators.endomorphic_operator import EndomorphicOperator
+from .multi_domain import MultiDomain
+from .multi_field import MultiField
+
+
+class BlockDiagonalOperator(EndomorphicOperator):
+    def __init__(self, operators):
+        """
+        Parameters
+        ----------
+        operators : dict
+            dictionary with operators domain names as keys and
+            LinearOperators as items
+        """
+        super(BlockDiagonalOperator, self).__init__()
+        self._operators = operators
+        self._domain = MultiDomain.make(
+            {key: op.domain for key, op in self._operators.items()})
+        self._cap = self._all_ops
+        for op in self._operators.values():
+            self._cap &= op.capability
+
+    @property
+    def domain(self):
+        return self._domain
+
+    @property
+    def capability(self):
+        return self._cap
+
+    def apply(self, x, mode):
+        self._check_input(x, mode)
+        return MultiField({key: op.apply(x[key], mode=mode)
+                           for key, op in self._operators.items()})
+
+    def draw_sample(self, from_inverse=False, dtype=np.float64):
+        dtype = MultiField.build_dtype(dtype, self._domain)
+        return MultiField({key: op.draw_sample(from_inverse, dtype[key])
+                           for key, op in self._operators.items()})
+
+    def _combine_chain(self, op):
+        res = {}
+        for key in self._operators.keys():
+            res[key] = self._operators[key]*op._operators[key]
+        return BlockDiagonalOperator(res)
+
+    def _combine_sum(self, op, selfneg, opneg):
+        from ..operators.sum_operator import SumOperator
+        res = {}
+        for key in self._operators.keys():
+            res[key] = SumOperator.make([self._operators[key],
+                                         op._operators[key]],
+                                        [selfneg, opneg])
+        return BlockDiagonalOperator(res)

nifty4/multi/multi_domain.py

-class MultiDomain(dict):
-    pass
+import collections
+from ..domain_tuple import DomainTuple
+
+__all = ["MultiDomain"]
+
+
+class frozendict(collections.Mapping):
+    """
+    An immutable wrapper around dictionaries that implements the complete
+    :py:class:`collections.Mapping` interface. It can be used as a drop-in
+    replacement for dictionaries where immutability is desired.
+    """
+
+    dict_cls = dict
+
+    def __init__(self, *args, **kwargs):
+        self._dict = self.dict_cls(*args, **kwargs)
+        self._hash = None
+
+    def __getitem__(self, key):
+        return self._dict[key]
+
+    def __contains__(self, key):
+        return key in self._dict
+
+    def copy(self, **add_or_replace):
+        return self.__class__(self, **add_or_replace)
+
+    def __iter__(self):
+        return iter(self._dict)
+
+    def __len__(self):
+        return len(self._dict)
+
+    def __repr__(self):
+        return '<%s %r>' % (self.__class__.__name__, self._dict)
+
+    def __hash__(self):
+        if self._hash is None:
+            h = 0
+            for key, value in self._dict.items():
+                h ^= hash((key, value))
+            self._hash = h
+        return self._hash
+
+
+class MultiDomain(frozendict):
+    _domainCache = {}
+
+    def __init__(self, domain, _callingfrommake=False):
+        if not _callingfrommake:
+            raise NotImplementedError
+        super(MultiDomain, self).__init__(domain)
+
+    @staticmethod
+    def make(domain):
+        if isinstance(domain, MultiDomain):
+            return domain
+        if not isinstance(domain, dict):
+            raise TypeError("dict expected")
+        tmp = {}
+        for key, value in domain.items():
+            if not isinstance(key, str):
+                raise TypeError("keys must be strings")
+            tmp[key] = DomainTuple.make(value)
+        domain = frozendict(tmp)
+        obj = MultiDomain._domainCache.get(domain)
+        if obj is not None:
+            return obj
+        obj = MultiDomain(domain, _callingfrommake=True)
+        MultiDomain._domainCache[domain] = obj
+        return obj

nifty4/multi/multi_field.py

@@ -44,7 +44,8 @@ class MultiField(object):