Merge branch 'master' into 'spherical_plots'

Master

See merge request !127

README.md

@@ -78,11 +78,6 @@ The current version of Nifty3 can be obtained by cloning the repository:
 
     git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
 
-and switching to the "master" branch:
-
-    cd NIFTy
-    git checkout master
-
 ### Installation on Ubuntu
 
 This is for you if you want to install NIFTy on your personal computer
@@ -98,14 +93,13 @@ Starting with a fresh Ubuntu installation move to a folder like
 
         git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
         cd pyHealpix
-        autoreconf -i && ./configure --prefix=$HOME/.local && make -j4 && make install
+        autoreconf -i && ./configure --prefix=$HOME/.local --enable-openmp --enable-native-optimizations && make -j4 && make install
         cd ..
 
 -   Finally, NIFTy:
 
         git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
         cd NIFTy
-        git checkout master
         python setup.py install --user
         cd ..
 
@@ -135,7 +129,7 @@ may cause trouble.
 
         git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
         cd pyHealpix
-        autoreconf -i && ./configure --prefix=`python-config --prefix` && make -j4 && sudo make install
+        autoreconf -i && ./configure --prefix=`python-config --prefix` --enable-openmp --enable-native-optimizations && make -j4 && sudo make install
         cd ..
 
     (The third command installs the package system-wide. User-specific
@@ -146,7 +140,6 @@ may cause trouble.
 
         git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
         cd NIFTy
-        git checkout master
         python setup.py install --user
         cd ..
 
@@ -197,4 +190,4 @@ The NIFTY package is licensed under the
 [1] Selig et al., "NIFTY - Numerical Information Field Theory - a
 versatile Python library for signal inference", [A&A, vol. 554, id.
 A26](http://dx.doi.org/10.1051/0004-6361/201321236), 2013;
-[arXiv:1301.4499](http://www.arxiv.org/abs/1301.4499)
\ No newline at end of file
+[arXiv:1301.4499](http://www.arxiv.org/abs/1301.4499)

ci/install_h5py.sh

 #!/bin/bash
 
-wget https://api.github.com/repos/h5py/h5py/tags -O - | grep tarball_url | grep -v rc | head -n 1 | cut -d '"' -f 4 | wget -i - -O h5py.tar.gz
-tar xzf h5py.tar.gz
-cd h5py-h5py*
 export CC=mpicc
 export HDF5_DIR=/usr/lib/x86_64-linux-gnu/hdf5/openmpi
-python setup.py configure --mpi
-python setup.py build
-python setup.py install
-cd ..
-rm -r h5py-h5py*
-rm h5py.tar.gz
+export HDF5_MPI="ON"
+pip install --no-binary=h5py h5py

ci/requirements.txt

 numpy
+cython
 mpi4py
 matplotlib
 plotly

ci/requirements_base.txt

 numpy
+cython
 nose
 parameterized
 coverage

demos/wiener_filter_harmonic - Kopie.py

-from nifty import *
-from mpi4py import MPI
-import plotly.offline as py
-import plotly.graph_objs as go
-
-
-comm = MPI.COMM_WORLD
-rank = comm.rank
-
-
-def plot_maps(x, name):
-
-    trace = [None]*len(x)
-
-    keys = x.keys()
-    field = x[keys[0]]
-    domain = field.domain[0]
-    shape = len(domain.shape)
-    max_n = domain.shape[0]*domain.distances[0]
-    step = domain.distances[0]
-    x_axis = np.arange(0, max_n, step)
-
-    if shape == 1:
-        for ii in xrange(len(x)):
-            trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
-        fig = go.Figure(data=trace)
-
-        py.plot(fig, filename=name)
-
-    elif shape == 2:
-        for ii in xrange(len(x)):
-            py.plot([go.Heatmap(z=x[keys[ii]].val.get_full_data().real)], filename=keys[ii])
-    else:
-        raise TypeError("Only 1D and 2D field plots are supported")
-
-def plot_power(x, name):
-
-    layout = go.Layout(
-        xaxis=dict(
-            type='log',
-            autorange=True
-        ),
-        yaxis=dict(
-            type='log',
-            autorange=True
-        )
-    )
-
-    trace = [None]*len(x)
-
-    keys = x.keys()
-    field = x[keys[0]]
-    domain = field.domain[0]
-    x_axis = domain.kindex
-
-    for ii in xrange(len(x)):
-        trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
-
-    fig = go.Figure(data=trace, layout=layout)
-    py.plot(fig, filename=name)
-
-np.random.seed(42)
-
-
-
-if __name__ == "__main__":
-
-    distribution_strategy = 'not'
-
-    # setting spaces
-    npix = np.array([500])  # number of pixels
-    total_volume = 1.  # total length
-
-    # setting signal parameters
-    lambda_s = .05  # signal correlation length
-    sigma_s = 10.  # signal variance
-
-
-    #setting response operator parameters
-    length_convolution = .025
-    exposure = 1.
-
-    # calculating parameters
-    k_0 = 4. / (2 * np.pi * lambda_s)
-    a_s = sigma_s ** 2. * lambda_s * total_volume
-
-    # creation of spaces
-#    x1 = RGSpace([npix,npix], distances=total_volume / npix,
-#                 zerocenter=False)
-#    k1 = RGRGTransformation.get_codomain(x1)
-    x1 = HPSpace(32)
-    k1 = HPLMTransformation.get_codomain(x1)
-
-    p1 = PowerSpace(harmonic_partner=k1, logarithmic=False)
-
-
-    # creating Power Operator with given spectrum
-    spec = (lambda k: a_s / (1 + (k / k_0) ** 2) ** 2)
-    p_field = Field(p1, val=spec)
-    S_op = create_power_operator(k1, spec)
-
-    # creating FFT-Operator and Response-Operator with Gaussian convolution
-    Fft_op = FFTOperator(domain=x1, target=k1,
-                        domain_dtype=np.float64,
-                        target_dtype=np.complex128)
-    R_op = ResponseOperator(x1, sigma=[length_convolution],
-                            exposure=[exposure])
-
-    # drawing a random field
-    sk = p_field.power_synthesize(real_signal=True, mean=0.)
-    s = Fft_op.adjoint_times(sk)
-
-    signal_to_noise = 1
-    N_op = DiagonalOperator(R_op.target, diagonal=s.var()/signal_to_noise, bare=True)
-    n = Field.from_random(domain=R_op.target,
-                          random_type='normal',
-                          std=s.std()/np.sqrt(signal_to_noise),
-                          mean=0.)
-
-    d = R_op(s) + n
-
-    # Wiener filter
-    j = Fft_op.times(R_op.adjoint_times(N_op.inverse_times(d)))
-    D = HarmonicPropagatorOperator(S=S_op, N=N_op, R=R_op)
-
-    mk = D(j)
-
-    m = Fft_op.adjoint_times(mk)
-
-#    z={}
-#    z["signal"] = s
-#    z["reconstructed_map"] = m
-#    z["data"] = d
-#    z["lambda"] = R_op(s)
-#    z["j"] = j
-#
-#    plot_maps(z, "Wiener_filter.html")
-
-

nifty/field.py

@@ -17,6 +17,8 @@
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 from __future__ import division
+
+import itertools
 import numpy as np
 
 from keepers import Versionable,\
@@ -104,6 +106,7 @@ class Field(Loggable, Versionable, object):
     distributed_data_object
 
     """
+
     # ---Initialization methods---
 
     def __init__(self, domain=None, val=None, dtype=None,
@@ -358,7 +361,7 @@ class Field(Loggable, Versionable, object):
                 self.domain_axes[space_index])
 
         harmonic_domain = self.domain[space_index]
-        power_domain = PowerSpace(harmonic_domain=harmonic_domain,
+        power_domain = PowerSpace(harmonic_partner=harmonic_domain,
                                   distribution_strategy=distribution_strategy,
                                   logarithmic=logarithmic, nbin=nbin,
                                   binbounds=binbounds)
@@ -512,7 +515,7 @@ class Field(Loggable, Versionable, object):
         result_domain = list(self.domain)
         for power_space_index in spaces:
             power_space = self.domain[power_space_index]
-            harmonic_domain = power_space.harmonic_domain
+            harmonic_domain = power_space.harmonic_partner
             result_domain[power_space_index] = harmonic_domain
 
         # create random samples: one or two, depending on whether the
@@ -554,14 +557,12 @@ class Field(Loggable, Versionable, object):
                                             inplace=True)
 
         if real_signal:
-            for power_space_index in spaces:
-                harmonic_domain = result_domain[power_space_index]
-                result_val_list = [harmonic_domain.hermitian_decomposition(
-                                    result_val,
-                                    axes=result.domain_axes[power_space_index],
-                                    preserve_gaussian_variance=True)[0]
-                                   for (result, result_val)
-                                   in zip(result_list, result_val_list)]
+            result_val_list = [self._hermitian_decomposition(
+                                                result_domain,
+                                                result_val,
+                                                spaces,
+                                                result_list[0].domain_axes)[0]
+                               for result_val in result_val_list]
 
         # store the result into the fields
         [x.set_val(new_val=y, copy=False) for x, y in
@@ -574,6 +575,46 @@ class Field(Loggable, Versionable, object):
 
         return result
 
+    @staticmethod
+    def _hermitian_decomposition(domain, val, spaces, domain_axes):
+        # 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 xrange(1, len(spaces)):
+            (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()
+            h = (val + c)/2.
+            a = (val - c)/2.
+
+        # correct variance
+        fixed_points = [domain[i].hermitian_fixed_points() for i in spaces]
+        # check if there was at least one flipping during hermitianization
+        flipped_Q = np.any([fp is not None for fp in fixed_points])
+        # if the array got flipped, correct the variance
+        if flipped_Q:
+            h *= np.sqrt(2)
+            a *= np.sqrt(2)
+            fixed_points = [[fp] if fp is None else fp for fp in fixed_points]
+            for product_point in itertools.product(*fixed_points):
+                slice_object = np.array((slice(None), )*len(val.shape),
+                                        dtype=np.object)
+                for i, sp in enumerate(spaces):
+                    point_component = product_point[i]
+                    if point_component is None:
+                        point_component = slice(None)
+                    slice_object[list(domain_axes[sp])] = point_component
+
+                slice_object = tuple(slice_object)
+                h[slice_object] /= np.sqrt(2)
+                a[slice_object] /= np.sqrt(2)
+
+        return (h, a)
+
     def _spec_to_rescaler(self, spec, result_list, power_space_index):
         power_space = self.domain[power_space_index]
 

nifty/spaces/lm_space/lm_space.py

@@ -94,9 +94,12 @@ class LMSpace(Space):
         hermitian_part = x.copy_empty()
         anti_hermitian_part = x.copy_empty()
         hermitian_part[:] = x.real
-        anti_hermitian_part[:] = x.imag
+        anti_hermitian_part[:] = x.imag * 1j
         return (hermitian_part, anti_hermitian_part)
 
+    def hermitian_fixed_points(self):
+        return None
+
     # ---Mandatory properties and methods---
 
     @property

nifty/spaces/power_space/power_space.py

@@ -114,6 +114,11 @@ class PowerSpace(Space):
         self._pundex = power_index['pundex']
         self._k_array = power_index['k_array']
 
+        if self.config['nbin'] is not None:
+            if self.config['nbin'] > len(self.kindex):
+                self.logger.warn("nbin was set to a value being larger than "
+                                 "the length of kindex!")
+
     def pre_cast(self, x, axes):
         """ Casts power spectrum functions to discretized power spectra.
 

nifty/spaces/rg_space/rg_space.py

@@ -110,7 +110,7 @@ class RGSpace(Space):
         hermitian_part /= 2.
 
         # use subtraction since it is faster than flipping another time
-        anti_hermitian_part = (x-hermitian_part)/1j
+        anti_hermitian_part = (x-hermitian_part)
 
         if preserve_gaussian_variance:
             hermitian_part, anti_hermitian_part = \
@@ -120,6 +120,18 @@ class RGSpace(Space):
 
         return (hermitian_part, anti_hermitian_part)
 
+    def hermitian_fixed_points(self):
+        shape = self.shape
+        mid_index = np.array(shape)//2
+        ndlist = [2 if (shape[i] % 2 == 0) else 1 for i in xrange(len(shape))]
+        ndlist = tuple(ndlist)
+        odd_axes_list = np.array([1 if (shape[i] % 2 == 1) else 0
+                                  for i in xrange(len(shape))])
+        fixed_points = []
+        for i in np.ndindex(ndlist):
+            fixed_points += [tuple((i+odd_axes_list) * mid_index)]
+        return fixed_points
+
     def _hermitianize_correct_variance(self, hermitian_part,
                                        anti_hermitian_part, axes):
         # Correct the variance by multiplying sqrt(2)
@@ -145,8 +157,9 @@ class RGSpace(Space):
         return hermitian_part, anti_hermitian_part
 
     def _hermitianize_inverter(self, x, axes):
+        shape = x.shape
         # calculate the number of dimensions the input array has
-        dimensions = len(x.shape)
+        dimensions = len(shape)
         # prepare the slicing object which will be used for mirroring
         slice_primitive = [slice(None), ] * dimensions
         # copy the input data
@@ -158,11 +171,17 @@ class RGSpace(Space):
         # flip in the desired directions
         for i in axes:
             slice_picker = slice_primitive[:]
-            slice_picker[i] = slice(1, None, None)
+            if shape[i] % 2 == 0:
+                slice_picker[i] = slice(1, None, None)
+            else:
+                slice_picker[i] = slice(None)
             slice_picker = tuple(slice_picker)
 
             slice_inverter = slice_primitive[:]
-            slice_inverter[i] = slice(None, 0, -1)
+            if shape[i] % 2 == 0:
+                slice_inverter[i] = slice(None, 0, -1)
+            else:
+                slice_inverter[i] = slice(None, None, -1)
             slice_inverter = tuple(slice_inverter)
 
             try:

test/test_serialization.py

@@ -21,31 +21,18 @@ import unittest
 from numpy.testing import assert_equal
 from keepers import Repository
 from test.common import expand, generate_spaces
+from nose.plugins.skip import SkipTest
 import os
 
-try:
-    import h5py
-except ImportError:
-    h5py_available = False
-else:
-    h5py_available = True
-
-if h5py_available:
-    class SpaceSerializationTests(unittest.TestCase):
-        # variable to hold the repository
-        _repo = None
-
-        @classmethod
-        def setUpClass(cls):
-            cls._repo = Repository('test.h5')
-
-        @expand([[space] for space in generate_spaces()])
-        def test_serialization(self, space):
-            self._repo.add(space, 'space')
-            self._repo.commit()
-
-            assert_equal(space, self._repo.get('space'))
-
-        @classmethod
-        def tearDownClass(cls):
-            os.remove('test.h5')
+class SpaceSerializationTests(unittest.TestCase):
+    @expand([[space] for space in generate_spaces()])
+    def test_serialization(self, space):
+        try:
+            import h5py
+        except ImportError:
+            raise SkipTest
+        repo = Repository('test.h5')
+        repo.add(space, 'space')
+        repo.commit()
+        assert_equal(space, repo.get('space'))
+        os.remove('test.h5')

test/test_spaces/test_lm_space.py

@@ -116,7 +116,7 @@ class LMSpaceFunctionalityTests(unittest.TestCase):
             real)
         assert_almost_equal(
             l.hermitian_decomposition(distributed_data_object(x))[1],
-            imag)
+            imag*1j)
 
     @expand(get_weight_configs())
     def test_weight(self, x, power, axes, inplace, expected):

test/test_spaces/test_rg_space.py

@@ -162,7 +162,7 @@ def get_hermitian_configs():
         [0.37928780+0.j, 0.33013206+0.26511434j, 0.48235714+0.j,
             0.33013206-0.26511434j],
         [0.47773437+0.17342852j, 0.31059374+0.02379381j, 0.64003551-0.23838932j,
-            0.27824437-0.0197826j]])
+            0.27824437-0.0197826j]])*1j
 
     h_1_x = np.array([
         [[0.23987021+0.41617749j, 0.34605012+0.55462234j, 0.07947035+0.73360723j,
@@ -205,7 +205,7 @@ def get_hermitian_configs():
              0.64466122-0.10318736j],
          [0.19076379+0.j, 0.49389371+0.03664104j, 0.85091112+0.j,
              0.49389371-0.03664104j]]
-        ])
+        ])*1j
     return [
         [h_0_x, None, h_0_res_real, h_0_res_imag],
         [h_1_x, (2,), h_1_res_real, h_1_res_imag]