merge NIFTy_4

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

@@ -31,7 +31,7 @@ d = R(s_x) + n
 
 R_p = R * FFT * A
 j = R_p.adjoint(N.inverse(d))
-D_inv = ift.SandwichOperator(R_p, N.inverse) + S.inverse
+D_inv = ift.SandwichOperator.make(R_p, N.inverse) + S.inverse
 
 
 N_samps = 200
@@ -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

@@ -80,12 +80,12 @@ if __name__ == "__main__":
     IC = ift.GradientNormController(name="inverter", iteration_limit=500,
                                     tol_abs_gradnorm=1e-3)
     inverter = ift.ConjugateGradient(controller=IC)
-    D = (ift.SandwichOperator(R, N.inverse) + Phi_h.inverse).inverse
+    D = (ift.SandwichOperator.make(R, N.inverse) + Phi_h.inverse).inverse
     D = ift.InversionEnabler(D, inverter, approximation=Phi_h)
     m = HT(D(j))
 
     # Uncertainty
-    D = ift.SandwichOperator(aHT, D)  # real space propagator
+    D = ift.SandwichOperator.make(aHT, D)  # real space propagator
     Dhat = ift.probe_with_posterior_samples(D.inverse, None,
                                             nprobes=nprobes)[1]
     sig = ift.sqrt(Dhat)
@@ -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_data_space_noiseless.py

@@ -13,19 +13,20 @@ class CustomResponse(ift.LinearOperator):
     def __init__(self, domain, data_points):
         self._domain = ift.DomainTuple.make(domain)
         self._points = data_points
-        data_shape = ift.Field.zeros(domain).val[data_points].shape
+        data_shape = ift.Field.full(domain, 0.).to_global_data()[data_points]\
+            .shape
         self._target = ift.DomainTuple.make(ift.UnstructuredDomain(data_shape))
 
     def _times(self, x):
-        d = ift.Field.zeros(self._target)
-        d.val[()] += x.val[self._points]
-        return d
+        d = np.zeros(self._target.shape, dtype=np.float64)
+        d += x.to_global_data()[self._points]
+        return ift.from_global_data(self._target, d)
 
     def _adjoint_times(self, d):
-        x = ift.Field.zeros(self._domain)
-        x.val[self._points] += d.val[()]
-        return x
-    
+        x = np.zeros(self._domain.shape, dtype=np.float64)
+        x[self._points] += d.to_global_data()
+        return ift.from_global_data(self._domain, x)
+
     @property
     def domain(self):
         return self._domain
@@ -61,8 +62,8 @@ if __name__ == "__main__":
 
     # Set up derived constants
     k_0 = 1./correlation_length
-    #defining a power spectrum with the right correlation length
-    #we later set the field variance to the desired value
+    # defining a power spectrum with the right correlation length
+    # we later set the field variance to the desired value
     unscaled_pow_spec = (lambda k: 1. / (1 + k/k_0) ** 4)
     pixel_width = L/N_pixels
 
@@ -71,7 +72,7 @@ if __name__ == "__main__":
     h_space = s_space.get_default_codomain()
     s_var = ift.get_signal_variance(unscaled_pow_spec, h_space)
     pow_spec = (lambda k: unscaled_pow_spec(k)/s_var*field_variance**2)
-    
+
     HT = ift.HarmonicTransformOperator(h_space, s_space)
 
     # Create mock data
@@ -79,7 +80,8 @@ if __name__ == "__main__":
     Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)
     sh = Sh.draw_sample()
 
-    Rx = CustomResponse(s_space, [np.arange(0,N_pixels, 5)[:,np.newaxis], np.arange(0,N_pixels, 2)[np.newaxis,:]])
+    Rx = CustomResponse(s_space, [np.arange(0, N_pixels, 5)[:, np.newaxis],
+                                  np.arange(0, N_pixels, 2)[np.newaxis, :]])
     ift.extra.consistency_check(Rx)
     a = ift.Field.from_random('normal', s_space)
     b = ift.Field.from_random('normal', Rx.target)
@@ -97,16 +99,17 @@ if __name__ == "__main__":
                                     tol_abs_gradnorm=0.0001)
     inverter = ift.ConjugateGradient(controller=IC)
     # setting up measurement precision matrix M
-    M = (ift.SandwichOperator(R.adjoint, Sh) + N)
+    M = (ift.SandwichOperator.make(R.adjoint, Sh) + N)
     M = ift.InversionEnabler(M, inverter)
     m = Sh(R.adjoint(M.inverse_times(d)))
 
     # Plotting
     backprojection = Rx.adjoint(d)
-    reweighted_backprojection = backprojection / backprojection.max() * HT(sh).max()
+    reweighted_backprojection = (backprojection / backprojection.max() *
+                                 HT(sh).max())
     zmax = max(HT(sh).max(), reweighted_backprojection.max(), HT(m).max())
     zmin = min(HT(sh).min(), reweighted_backprojection.min(), HT(m).min())
     plotdict = {"colormap": "Planck-like", "zmax": zmax, "zmin": zmin}
     ift.plot(HT(sh), name="mock_signal.png", **plotdict)
-    ift.plot(backprojection , name="backprojected_data.png", **plotdict)
+    ift.plot(backprojection, name="backprojected_data.png", **plotdict)
     ift.plot(HT(m), name="reconstruction.png", **plotdict)

demos/wiener_filter_easy.py

@@ -31,7 +31,7 @@ if __name__ == "__main__":
     h_space = s_space.get_default_codomain()
     s_var = ift.get_signal_variance(unscaled_pow_spec, h_space)
     pow_spec = (lambda k: unscaled_pow_spec(k)/s_var*field_variance**2)
-    
+
     HT = ift.HarmonicTransformOperator(h_space, s_space)
 
     # Create mock data
@@ -53,7 +53,7 @@ if __name__ == "__main__":
     IC = ift.GradientNormController(name="inverter", iteration_limit=500,
                                     tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=IC)
-    D = (ift.SandwichOperator(R, N.inverse) + Sh.inverse).inverse
+    D = (ift.SandwichOperator.make(R, N.inverse) + Sh.inverse).inverse
     D = ift.InversionEnabler(D, inverter, approximation=Sh)
     m = D(j)
 

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