merge from master

demos/critical_filtering.py

-
 from nifty import *
 from nifty.library.wiener_filter import WienerFilterEnergy
 from nifty.library.critical_filter import CriticalPowerEnergy
@@ -113,7 +112,7 @@ if __name__ == "__main__":
         print (x, iteration)
 
 
-    minimizer1 = RelaxedNewton(convergence_tolerance=10e-2,
+    minimizer1 = RelaxedNewton(convergence_tolerance=1e-2,
                               convergence_level=2,
                               iteration_limit=3,
                               callback=convergence_measure)
@@ -124,7 +123,7 @@ if __name__ == "__main__":
                        max_history_length=3)
 
     # Setting starting position
-    flat_power = Field(p_space,val=10e-8)
+    flat_power = Field(p_space,val=1e-8)
     m0 = flat_power.power_synthesize(real_signal=True)
 
     t0 = Field(p_space, val=log(1./(1+p_space.kindex)**2))
@@ -136,7 +135,7 @@ if __name__ == "__main__":
                               distribution_strategy=distribution_strategy)
 
         # Initializing the  nonlinear Wiener Filter energy
-        map_energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0, inverter=inverter)
+        map_energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0)
         # Solving the Wiener Filter analytically
         D0 = map_energy.curvature
         m0 = D0.inverse_times(j)

demos/wiener_filter_advanced.py

-
 from nifty import *
 
 import plotly.offline as pl
 import plotly.graph_objs as go
+from nifty.library.wiener_filter import *
 
 from mpi4py import MPI
 comm = MPI.COMM_WORLD
@@ -103,7 +103,7 @@ if __name__ == "__main__":
     #
 
     inverter = ConjugateGradient(convergence_level=3,
-                                 convergence_tolerance=10e-5,
+                                 convergence_tolerance=1e-5,
                                  preconditioner=None)
     # Setting starting position
     m0 = Field(h_space, val=.0)

demos/wiener_filter_easy.py

+import numpy as np
+
+from nifty import RGSpace, PowerSpace, Field, FFTOperator, ComposedOperator,\
+                  SmoothingOperator, DiagonalOperator, create_power_operator
+from nifty.library import WienerFilterCurvature
 
-from nifty import *
 #import plotly.offline as pl
 #import plotly.graph_objs as go
 
@@ -10,36 +14,37 @@ rank = comm.rank
 
 if __name__ == "__main__":
 
-    distribution_strategy = 'not'
-    
-    #Setting up physical constants
-    #total length of Interval or Volume the field lives on, e.g. in meters
+    distribution_strategy = 'fftw'
+
+    # Setting up physical constants
+    # total length of Interval or Volume the field lives on, e.g. in meters
     L = 2.
-    #typical distance over which the field is correlated (in same unit as L)
+    # typical distance over which the field is correlated (in same unit as L)
     correlation_length = 0.1
-    #variance of field in position space sqrt(<|s_x|^2>) (in unit of s)
+    # variance of field in position space sqrt(<|s_x|^2>) (in unit of s)
     field_variance = 2.
-    #smoothing length of response (in same unit as L)
+    # smoothing length of response (in same unit as L)
     response_sigma = 0.1
-    
-    #defining resolution (pixels per dimension)
+
+    # defining resolution (pixels per dimension)
     N_pixels = 512
-    
-    #Setting up derived constants
+
+    # Setting up derived constants
     k_0 = 1./correlation_length
-    #note that field_variance**2 = a*k_0/4. for this analytic form of power
-    #spectrum
+    # note that field_variance**2 = a*k_0/4. for this analytic form of power
+    # spectrum
     a = field_variance**2/k_0*4.
     pow_spec = (lambda k: a / (1 + k/k_0) ** 4)
-    pixel_width = L/N_pixels
-    
+    pixel_length = L/N_pixels
+
     # Setting up the geometry
-    s_space = RGSpace([N_pixels, N_pixels], distances = pixel_width)
-    fft = FFTOperator(s_space)
+    s_space = RGSpace([N_pixels, N_pixels], distances=pixel_length)
+    fft = FFTOperator(s_space, domain_dtype=np.float, target_dtype=np.complex)
     h_space = fft.target[0]
+    inverse_fft = FFTOperator(h_space, target=s_space,
+                              domain_dtype=np.complex, target_dtype=np.float)
     p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)
 
-
     # Creating the mock data
 
     S = create_power_operator(h_space, power_spectrum=pow_spec,
@@ -51,6 +56,7 @@ if __name__ == "__main__":
     ss = fft.inverse_times(sh)
 
     R = SmoothingOperator(s_space, sigma=response_sigma)
+    R_harmonic = ComposedOperator([inverse_fft, R], default_spaces=[0, 0])
 
     signal_to_noise = 1
     N = DiagonalOperator(s_space, diagonal=ss.var()/signal_to_noise, bare=True)
@@ -63,7 +69,9 @@ if __name__ == "__main__":
 
     # Wiener filter
 
-    j = R.adjoint_times(N.inverse_times(d))
-    D = PropagatorOperator(S=S, N=N, R=R)
+    j = R_harmonic.adjoint_times(N.inverse_times(d))
+    wiener_curvature = WienerFilterCurvature(S=S, N=N, R=R_harmonic)
+
+    m = wiener_curvature.inverse_times(j)
+    m_s = inverse_fft(m)
 
-    m = D(j)

nifty/config/nifty_config.py

@@ -66,7 +66,7 @@ variable_default_field_dtype = keepers.Variable(
 variable_default_distribution_strategy = keepers.Variable(
                               'default_distribution_strategy',
                               ['not', 'fftw', 'equal'],
-                              lambda z: (('pyfftw' in dependency_injector)
+                              lambda z: (('fftw' in dependency_injector)
                                          if z == 'fftw' else True),
                               genus='str')
 

nifty/field.py

@@ -596,22 +596,15 @@ class Field(Loggable, Versionable, object):
     @staticmethod
     def _hermitian_decomposition(domain, val, spaces, domain_axes,
                                  preserve_gaussian_variance=False):
-        # hermitianize for the first space
-        (h, a) = domain[spaces[0]].hermitian_decomposition(
-                       val,
-                       domain_axes[spaces[0]])
-        # hermitianize all remaining spaces using the iterative formula
-        for space in spaces[1:]:
-            (hh, ha) = domain[space].hermitian_decomposition(
-                                              h,
-                                              domain_axes[space])
-            (ah, aa) = domain[space].hermitian_decomposition(
-                                              a,
-                                              domain_axes[space])
-            c = (hh - ha - ah + aa).conjugate()
-            full = (hh + ha + ah + aa)
-            h = (full + c)/2.
-            a = (full - c)/2.
+
+        flipped_val = val
+        for space in spaces:
+            flipped_val = domain[space].hermitianize_inverter(
+                                                    x=flipped_val,
+                                                    axes=domain_axes[space])
+        flipped_val = flipped_val.conjugate()
+        h = (val + flipped_val)/2.
+        a = val - h
 
         # correct variance
         if preserve_gaussian_variance:

nifty/operators/fft_operator/fft_operator.py

@@ -117,7 +117,6 @@ class FFTOperator(LinearOperator):
         super(FFTOperator, self).__init__(default_spaces)
 
         # Initialize domain and target
-
         self._domain = self._parse_domain(domain)
         if len(self.domain) != 1:
             raise ValueError("TransformationOperator accepts only exactly one "

nifty/operators/smoothing_operator/fft_smoothing_operator.py

@@ -8,12 +8,21 @@ from .smoothing_operator import SmoothingOperator
 
 class FFTSmoothingOperator(SmoothingOperator):
 
-    def _smooth(self, x, spaces, inverse):
-        Transformator = FFTOperator(x.domain[spaces[0]])
+    def __init__(self, domain, sigma, log_distances=False,
+                 default_spaces=None):
+        super(FFTSmoothingOperator, self).__init__(
+                                                domain=domain,
+                                                sigma=sigma,
+                                                log_distances=log_distances,
+                                                default_spaces=default_spaces)
+        self._transformator_cache = {}
 
+    def _smooth(self, x, spaces, inverse):
         # transform to the (global-)default codomain and perform all remaining
         # steps therein
-        transformed_x = Transformator(x, spaces=spaces)
+        transformator = self._get_transformator(x.dtype)
+
+        transformed_x = transformator(x, spaces=spaces)
         codomain = transformed_x.domain[spaces[0]]
         coaxes = transformed_x.domain_axes[spaces[0]]
 
@@ -51,9 +60,18 @@ class FFTSmoothingOperator(SmoothingOperator):
 
         transformed_x.val.set_local_data(local_transformed_x, copy=False)
 
-        smoothed_x = Transformator.adjoint_times(transformed_x, spaces=spaces)
+        smoothed_x = transformator.adjoint_times(transformed_x,
+                                                 spaces=spaces)
 
         result = x.copy_empty()
         result.set_val(smoothed_x, copy=False)
 
         return result
+
+    def _get_transformator(self, dtype):
+        if dtype not in self._transformator_cache:
+            self._transformator_cache[dtype] = FFTOperator(
+                                                    self.domain,
+                                                    domain_dtype=dtype,
+                                                    target_dtype=np.complex)
+        return self._transformator_cache[dtype]

nifty/spaces/lm_space/lm_space.py

@@ -89,22 +89,6 @@ class LMSpace(Space):
         super(LMSpace, self).__init__()
         self._lmax = self._parse_lmax(lmax)
 
-    def hermitian_decomposition(self, x, axes=None):
-        if issubclass(x.dtype.type, np.complexfloating):
-            hermitian_part = x.copy_empty()
-            anti_hermitian_part = x.copy_empty()
-            hermitian_part[:] = x.real
-            anti_hermitian_part[:] = x.imag * 1j
-        else:
-            hermitian_part = x.copy()
-            anti_hermitian_part = x.copy_empty()
-            anti_hermitian_part[:] = 0
-
-        return (hermitian_part, anti_hermitian_part)
-
-    def hermitian_fixed_points(self):
-        return None
-
     # ---Mandatory properties and methods---
 
     def __repr__(self):

nifty/spaces/power_space/power_indices.py

@@ -300,6 +300,9 @@ class PowerIndices(object):
             (temp_uniqued_pindex, local_temp_pundex) = np.unique(
                                                             local_pindex,
                                                             return_index=True)
+            # cure a bug in numpy
+            # https://github.com/numpy/numpy/issues/9405
+            local_temp_pundex = np.asarray(local_temp_pundex, dtype=np.int)
             # Shift the local pundices by the nodes' local_dim_offset
             local_temp_pundex += global_pindex.distributor.local_dim_offset
 

nifty/spaces/rg_space/rg_space.py

@@ -91,26 +91,6 @@ class RGSpace(Space):
         self._shape = self._parse_shape(shape)
         self._distances = self._parse_distances(distances)
 
-    def hermitian_decomposition(self, x, axes=None,
-                                preserve_gaussian_variance=False):
-        # check axes
-        if axes is None:
-            axes = range(len(self.shape))
-        assert len(x.shape) >= len(self.shape), "shapes mismatch"
-        assert len(axes) == len(self.shape), "axes mismatch"
-
-        # compute the hermitian part
-        flipped_x = self._hermitianize_inverter(x, axes=axes)
-        flipped_x = flipped_x.conjugate()
-        # average x and flipped_x.
-        hermitian_part = x + flipped_x
-        hermitian_part /= 2.
-
-        # use subtraction since it is faster than flipping another time
-        anti_hermitian_part = (x-hermitian_part)
-
-        return (hermitian_part, anti_hermitian_part)
-
     def hermitian_fixed_points(self):
         dimensions = len(self.shape)
         mid_index = np.array(self.shape)//2
@@ -124,7 +104,7 @@ class RGSpace(Space):
             fixed_points += [tuple(index * mid_index)]
         return fixed_points
 
-    def _hermitianize_inverter(self, x, axes):
+    def hermitianize_inverter(self, x, axes):
         # calculate the number of dimensions the input array has
         dimensions = len(x.shape)
         # prepare the slicing object which will be used for mirroring

nifty/spaces/space/space.py

@@ -147,35 +147,34 @@ class Space(DomainObject):
         raise NotImplementedError(
             "There is no generic co-smoothing kernel for Space base class.")
 
-    def hermitian_decomposition(self, x, axes,
-                                preserve_gaussian_variance=False):
-        """ Decomposes x into its hermitian and anti-hermitian constituents.
+    def hermitian_fixed_points(self):
+        """ Returns the array points which remain invariant under the action
+        of `hermitianize_inverter`
 
-        This method decomposes a field's array x into its hermitian and
-        antihermitian part, which corresponds to  real and imaginary part
-        in a corresponding harmonic partner space. This is an internal function
-        that is mainly used for power-synthesizing and -analyzing Fields.
+
+        Returns
+        -------
+        list of index-tuples
+            The list contains the index-coordinates of the invariant points.
+
+        """
+        return None
+
+    def hermitianize_inverter(self, x, axes):
+        """ Inverts/flips x in the context of Hermitian decomposition.
+
+        This method is mainly used for power-synthesizing and -analyzing
+        Fields.
 
         Parameters
         ----------
-        x : distributed_data_object
-            The field's val object.
         axes : tuple of ints
             Specifies the axes of x which correspond to this space.
 
-        preserve_gaussian_variance : bool *optional*
-            If the hermitian decomposition is done via computing the half
-            sums and differences of `x` and mirrored `x`, all points except the
-            fixed points lose half of their variance. If `x` is complex also
-            they lose half of their variance since the real(/imaginary) part
-            gets lost.
-
         Returns
         -------
-        (distributed_data_object, distributed_data_object)
-            A tuple of two distributed_data_objects, the first being the
-            hermitian and the second the anti-hermitian part of x.
-
+        distributed_data_object
+            The Hermitian-flipped of x.
         """
 
-        raise NotImplementedError
+        return x

test/test_spaces/test_lm_space.py

@@ -20,7 +20,7 @@ import unittest
 import numpy as np
 
 from numpy.testing import assert_, assert_equal, assert_raises,\
-        assert_almost_equal
+        assert_almost_equal, assert_array_almost_equal
 from d2o import distributed_data_object
 from nifty import LMSpace
 from test.common import expand
@@ -108,15 +108,12 @@ class LMSpaceFunctionalityTests(unittest.TestCase):
             for key, value in expected.iteritems():
                 assert_equal(getattr(l, key), value)
 
-    @expand(get_hermitian_configs())
-    def test_hermitian_decomposition(self, x, real, imag):
+    def test_hermitianize_inverter(self):
         l = LMSpace(5)
-        assert_almost_equal(
-            l.hermitian_decomposition(distributed_data_object(x))[0],
-            real)
-        assert_almost_equal(
-            l.hermitian_decomposition(distributed_data_object(x))[1],
-            imag*1j)
+        v = distributed_data_object(global_shape=l.shape, dtype=np.complex128)
+        v[:] = np.random.random(l.shape) + 1j*np.random.random(l.shape)
+        inverted = l.hermitianize_inverter(v, axes=(0,))
+        assert_array_almost_equal(inverted.get_full_data(), v.get_full_data())
 
     @expand(get_weight_configs())
     def test_weight(self, x, power, axes, inplace, expected):

test/test_spaces/test_power_space.py

@@ -143,7 +143,8 @@ class PowerSpaceConsistencyCheck(unittest.TestCase):
                        distribution_strategy=distribution_strategy,
                        logarithmic=logarithmic, nbin=nbin,
                        binbounds=binbounds)
-        assert_equal(p.pindex.flatten()[p.pundex], np.arange(p.dim),
+        assert_equal(p.pindex.flatten().get_full_data()[p.pundex],
+                     np.arange(p.dim),
                      err_msg='pundex is not right-inverse of pindex!')
 
     @expand(CONSISTENCY_CONFIGS)
@@ -152,7 +153,6 @@ class PowerSpaceConsistencyCheck(unittest.TestCase):
                                   logarithmic):
         if distribution_strategy == "fftw":
             if not hasattr(gdi.get('fftw'), 'FFTW_MPI'):
-                print (gdi.get('fftw'), "blub \n\n\n")
                 raise SkipTest
         p = PowerSpace(harmonic_partner=harmonic_partner,
                            distribution_strategy=distribution_strategy,

test/test_spaces/test_rg_space.py

@@ -21,6 +21,8 @@ from __future__ import division
 import unittest
 import numpy as np
 
+from d2o import distributed_data_object
+
 from numpy.testing import assert_, assert_equal, assert_almost_equal
 from nifty import RGSpace
 from test.common import expand
@@ -127,49 +129,21 @@ class RGSpaceFunctionalityTests(unittest.TestCase):
 
     @expand(product([(10,), (11,), (1, 1), (4, 4), (5, 7), (8, 12), (7, 16),
                      (4, 6, 8), (17, 5, 3)]))
-    def test_hermitian_decomposition(self, shape):
+    def test_hermitianize_inverter(self, shape):
         r = RGSpace(shape, harmonic=True)
-        v = np.empty(shape, dtype=np.complex128)
-        v.real = np.random.random(shape)
-        v.imag = np.random.random(shape)
-        h, a = r.hermitian_decomposition(v)
-        # make sure that data == h + a
-        # NOTE: this is only correct for preserve_gaussian_variance==False,
-        #       but I consider this an intrinsic property of a hermitian
-        #       decomposition.
-        assert_almost_equal(v, h+a)
-        print (h, a)
-
-        # test hermitianity of h
-        it = np.nditer(h, flags=['multi_index'])
-        while not it.finished:
-            i1 = it.multi_index
-            i2 = []
-            for i in range(len(i1)):
-                i2.append(h.shape[i]-i1[i] if i1[i] > 0 else 0)
-            i2 = tuple(i2)
-            assert_almost_equal(h[i1], np.conj(h[i2]))
-            assert_almost_equal(a[i1], -np.conj(a[i2]))
-            it.iternext()
+        v = distributed_data_object(global_shape=shape, dtype=np.complex128)
+        v[:] = np.random.random(shape) + 1j*np.random.random(shape)
+        inverted = r.hermitianize_inverter(v, axes=range(len(shape)))
 
-    @expand(product([(10,), (11,), (1, 1), (4, 4), (5, 7), (8, 12), (7, 16),
-                     (4, 6, 8), (17, 5, 3)]))
-    def test_hermitian_decomposition2(self, shape):
-        r = RGSpace(shape, harmonic=True)
-        v = np.random.random(shape)
-        h, a = r.hermitian_decomposition(v)
-        # make sure that data == h + a
-        assert_almost_equal(v, h+a)
-        # test hermitianity of h
-        it = np.nditer(h, flags=['multi_index'])
+        # test hermitian flipping of `inverted`
+        it = np.nditer(v, flags=['multi_index'])
         while not it.finished:
             i1 = it.multi_index
             i2 = []
             for i in range(len(i1)):
-                i2.append(h.shape[i]-i1[i] if i1[i] > 0 else 0)
+                i2.append(v.shape[i]-i1[i] if i1[i] > 0 else 0)
             i2 = tuple(i2)
-            assert_almost_equal(h[i1], np.conj(h[i2]))
-            assert_almost_equal(a[i1], -np.conj(a[i2]))
+            assert_almost_equal(inverted[i1], v[i2])
             it.iternext()
 
     @expand(get_distance_array_configs())