first steps

demos/probing.py

 # -*- coding: utf-8 -*-
 
+from __future__ import print_function
 from nifty import Field, RGSpace, DiagonalProberMixin, TraceProberMixin,\
                   Prober, DiagonalOperator
 
@@ -19,12 +20,12 @@ diagOp = DiagonalOperator(domain=x, diagonal=f)
 
 diagProber = DiagonalProber(domain=x)
 diagProber(diagOp)
-print (f - diagProber.diagonal).norm()
+print((f - diagProber.diagonal).norm())
 
 multiProber = MultiProber(domain=x)
 multiProber(diagOp)
-print (f - multiProber.diagonal).norm()
-print f.sum() - multiProber.trace
+print((f - multiProber.diagonal).norm())
+print(f.sum() - multiProber.trace)
 
 
 

demos/wiener_filter.py

+from __future__ import division
 
 from nifty import *
 #import plotly.offline as pl
@@ -11,7 +12,7 @@ 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
     L = 2.
@@ -21,18 +22,18 @@ if __name__ == "__main__":
     field_variance = 2.
     #smoothing length that response (in same unit as L)
     response_sigma = 0.1
-    
+
     #defining resolution (pixels per dimension)
     N_pixels = 512
-    
+
     #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
     a = field_variance**2/k_0*4.
-    pow_spec = (lambda k: a / (1 + k/k_0) ** 4)
+    pow_spec = (lambda k: a / (1. + k/k_0) ** 4)
     pixel_width = L/N_pixels
-    
+
     # Setting up the geometry
     s_space = RGSpace([N_pixels, N_pixels], distances = pixel_width)
     fft = FFTOperator(s_space)

demos/wiener_filter_hamiltonian.py

+from __future__ import division
+from __future__ import print_function
+from builtins import object
 
 from nifty import *
 
@@ -62,11 +65,11 @@ if __name__ == "__main__":
     p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)
 
     # create the field instances and power operator
-    pow_spec = (lambda k: (42 / (k + 1) ** 3))
+    pow_spec = (lambda k: (42. / (k + 1) ** 3))
     S = create_power_operator(h_space, power_spectrum=pow_spec,
                               distribution_strategy=distribution_strategy)
 
-    sp = Field(p_space, val=lambda z: pow_spec(z)**(1./2),
+    sp = Field(p_space, val=lambda z: pow_spec(z)**0.5,
                distribution_strategy=distribution_strategy)
     sh = sp.power_synthesize(real_signal=True)
     ss = fft.inverse_times(sh)
@@ -92,7 +95,7 @@ if __name__ == "__main__":
 
     def distance_measure(energy, iteration):
         x = energy.position
-        print (iteration, ((x-ss).norm()/ss.norm()).real)
+        print((iteration, (x-ss).norm()/ss.norm()).real))
 
 #    minimizer = SteepestDescent(convergence_tolerance=0,
 #                                iteration_limit=50,

demos/wiener_filter_harmonic.py

+from __future__ import division
+from builtins import range
 from nifty import *
 from mpi4py import MPI
 import plotly.offline as py
@@ -12,7 +14,7 @@ def plot_maps(x, name):
 
     trace = [None]*len(x)
 
-    keys = x.keys()
+    keys = list(x.keys())
     field = x[keys[0]]
     domain = field.domain[0]
     shape = len(domain.shape)
@@ -21,14 +23,14 @@ def plot_maps(x, name):
     x_axis = np.arange(0, max_n, step)
 
     if shape == 1:
-        for ii in xrange(len(x)):
+        for ii in range(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)):
+        for ii in range(len(x)):
             py.plot([go.Heatmap(z=x[keys[ii]].val.get_full_data())], filename=keys[ii])
     else:
         raise TypeError("Only 1D and 2D field plots are supported")
@@ -48,12 +50,12 @@ def plot_power(x, name):
 
     trace = [None]*len(x)
 
-    keys = x.keys()
+    keys = list(x.keys())
     field = x[keys[0]]
     domain = field.domain[0]
     x_axis = domain.kindex
 
-    for ii in xrange(len(x)):
+    for ii in range(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)

demos/wiener_filter_unit.py

+from __future__ import division
+from builtins import range
 from nifty import *
 from mpi4py import MPI
 import plotly.offline as py
@@ -12,7 +14,7 @@ def plot_maps(x, name):
 
     trace = [None]*len(x)
 
-    keys = x.keys()
+    keys = list(x.keys())
     field = x[keys[0]]
     domain = field.domain[0]
     shape = len(domain.shape)
@@ -21,14 +23,14 @@ def plot_maps(x, name):
     x_axis = np.arange(0, max_n, step)
 
     if shape == 1:
-        for ii in xrange(len(x)):
+        for ii in range(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)):
+        for ii in range(len(x)):
             py.plot([go.Heatmap(z=x[keys[ii]].val.get_full_data())], filename=keys[ii])
     else:
         raise TypeError("Only 1D and 2D field plots are supported")
@@ -48,12 +50,12 @@ def plot_power(x, name):
 
     trace = [None]*len(x)
 
-    keys = x.keys()
+    keys = list(x.keys())
     field = x[keys[0]]
     domain = field.domain[0]
     x_axis = domain.kindex
 
-    for ii in xrange(len(x)):
+    for ii in range(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)

nifty/__init__.py

@@ -26,32 +26,32 @@ logger = MPILogger()
 
 # it is important to import config before d2o such that NIFTy is able to
 # pre-create d2o's configuration object with the corrected path
-from config import dependency_injector,\
+from .config import dependency_injector,\
                    nifty_configuration,\
                    d2o_configuration
 
 from d2o import distributed_data_object, d2o_librarian
 
-from energies import *
+from .energies import *
 
-from field import Field
+from .field import Field
 
-from random import Random
+from .random import Random
 
-from basic_arithmetics import *
+from .basic_arithmetics import *
 
-from nifty_utilities import *
+from .nifty_utilities import *
 
-from field_types import *
+from .field_types import *
 
-from minimization import *
+from .minimization import *
 
-from spaces import *
+from .spaces import *
 
-from operators import *
+from .operators import *
 
-from probing import *
+from .probing import *
 
-from sugar import *
+from .sugar import *
 
-import plotting
+from . import plotting

nifty/basic_arithmetics.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.
 
+from __future__ import division
 import numpy as np
 from d2o import distributed_data_object
 from nifty.field import Field

nifty/config/__init__.py

@@ -17,7 +17,7 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 
-from nifty_config import dependency_injector,\
+from .nifty_config import dependency_injector,\
                          nifty_configuration
 
-from d2o_config import d2o_configuration
+from .d2o_config import d2o_configuration

nifty/config/nifty_config.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.
 
-import matplotlib_init
+from . import matplotlib_init
 
 import os
 

nifty/domain_object.py

@@ -16,14 +16,17 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
+from __future__ import division
 import abc
 from nifty.nifty_meta import NiftyMeta
 
 from keepers import Loggable,\
                     Versionable
+from future.utils import with_metaclass
 
 
-class DomainObject(Versionable, Loggable, object):
+class DomainObject(with_metaclass(NiftyMeta, type('NewBase',
+        (Versionable, Loggable, object), {}))):
     """The abstract class that can be used as a domain for a field.
 
     This holds all the information and functionality a field needs to know
@@ -39,8 +42,6 @@ class DomainObject(Versionable, Loggable, object):
 
     """
 
-    __metaclass__ = NiftyMeta
-
     def __init__(self):
         # _global_id is used in the Versioning module from keepers
         self._ignore_for_hash = ['_global_id']
@@ -56,7 +57,7 @@ class DomainObject(Versionable, Loggable, object):
             item = vars(self)[key]
             if key in self._ignore_for_hash or key == '_ignore_for_hash':
                 continue
-            result_hash ^= item.__hash__() ^ int(hash(key)/117)
+            result_hash ^= item.__hash__() ^ int(hash(key)//117)
         return result_hash
 
     def __eq__(self, x):
@@ -75,7 +76,7 @@ class DomainObject(Versionable, Loggable, object):
         """
 
         if isinstance(x, type(self)):
-            for key in vars(self).keys():
+            for key in list(vars(self).keys()):
                 item1 = vars(self)[key]
                 if key in self._ignore_for_hash or key == '_ignore_for_hash':
                     continue

nifty/energies/__init__.py

@@ -16,6 +16,6 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from energy import Energy
-from line_energy import LineEnergy
-from memoization import memo
+from .energy import Energy
+from .line_energy import LineEnergy
+from .memoization import memo

nifty/energies/energy.py

@@ -19,9 +19,10 @@
 from nifty.nifty_meta import NiftyMeta
 
 from keepers import Loggable
+from future.utils import with_metaclass
 
 
-class Energy(Loggable, object):
+class Energy(with_metaclass(NiftyMeta, type('NewBase', (Loggable, object), {}))):
     """ Provides the functional used by minimization schemes.
 
    The Energy object is an implementation of a scalar function including its
@@ -63,8 +64,6 @@ class Energy(Loggable, object):
 
     """
 
-    __metaclass__ = NiftyMeta
-
     def __init__(self, position):
         self._cache = {}
         try:

nifty/field.py

@@ -17,6 +17,9 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 from __future__ import division
+from builtins import zip
+from builtins import str
+from builtins import range
 
 import itertools
 import numpy as np
@@ -35,6 +38,7 @@ from nifty.spaces.power_space import PowerSpace
 
 import nifty.nifty_utilities as utilities
 from nifty.random import Random
+from functools import reduce
 
 
 class Field(Loggable, Versionable, object):
@@ -337,7 +341,7 @@ class Field(Loggable, Versionable, object):
         # check if the `spaces` input is valid
         spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
         if spaces is None:
-            spaces = range(len(self.domain))
+            spaces = list(range(len(self.domain)))
 
         if len(spaces) == 0:
             raise ValueError(
@@ -518,7 +522,7 @@ class Field(Loggable, Versionable, object):
         spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
 
         if spaces is None:
-            spaces = range(len(self.domain))
+            spaces = list(range(len(self.domain)))
 
         for power_space_index in spaces:
             power_space = self.domain[power_space_index]
@@ -602,7 +606,7 @@ class Field(Loggable, Versionable, object):
                        domain_axes[spaces[0]],
                        preserve_gaussian_variance=preserve_gaussian_variance)
         # hermitianize all remaining spaces using the iterative formula
-        for space in xrange(1, len(spaces)):
+        for space in range(1, len(spaces)):
             (hh, ha) = domain[space].hermitian_decomposition(
                                               h,
                                               domain_axes[space],
@@ -969,7 +973,7 @@ class Field(Loggable, Versionable, object):
 
         fast_copyable = True
         try:
-            for i in xrange(len(self.domain)):
+            for i in range(len(self.domain)):
                 if self.domain[i] is not domain[i]:
                     fast_copyable = False
                     break
@@ -991,7 +995,7 @@ class Field(Loggable, Versionable, object):
         # repair its class
         new_field.__class__ = self.__class__
         # copy domain, codomain and val
-        for key, value in self.__dict__.items():
+        for key, value in list(self.__dict__.items()):
             if key != '_val':
                 new_field.__dict__[key] = value
             else:
@@ -1028,7 +1032,7 @@ class Field(Loggable, Versionable, object):
 
         spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
         if spaces is None:
-            spaces = range(len(self.domain))
+            spaces = list(range(len(self.domain)))
 
         for ind, sp in enumerate(self.domain):
             if ind in spaces:
@@ -1166,7 +1170,7 @@ class Field(Loggable, Versionable, object):
     def _contraction_helper(self, op, spaces):
         # build a list of all axes
         if spaces is None:
-            spaces = xrange(len(self.domain))
+            spaces = range(len(self.domain))
         else:
             spaces = utilities.cast_axis_to_tuple(spaces, len(self.domain))
 
@@ -1186,7 +1190,7 @@ class Field(Loggable, Versionable, object):
             return data
         else:
             return_domain = tuple(self.domain[i]
-                                  for i in xrange(len(self.domain))
+                                  for i in range(len(self.domain))
                                   if i not in spaces)
 
             return_field = Field(domain=return_domain,
@@ -1234,7 +1238,7 @@ class Field(Loggable, Versionable, object):
         if isinstance(other, Field):
             try:
                 assert len(other.domain) == len(self.domain)
-                for index in xrange(len(self.domain)):
+                for index in range(len(self.domain)):
                     assert other.domain[index] == self.domain[index]
             except AssertionError:
                 raise ValueError(
@@ -1373,6 +1377,17 @@ class Field(Loggable, Versionable, object):
 
         return self._binary_helper(other, op='__rdiv__')
 
+    def __rtruediv__(self, other):
+        """ x.__rtruediv__(y) <==> y/x
+
+        See Also
+        --------
+        _builtin_helper
+
+        """
+
+        return self._binary_helper(other, op='__rtruediv__')
+
     def __idiv__(self, other):
         """ x.__idiv__(y) <==> x/=y
 

nifty/field_types/__init__.py

@@ -16,5 +16,5 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from field_type import FieldType
-from field_array import FieldArray
+from .field_type import FieldType
+from .field_array import FieldArray

nifty/field_types/field_array.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_type import FieldType
+from .field_type import FieldType
+from functools import reduce
 
 
 class FieldArray(FieldType):

nifty/minimization/__init__.py

@@ -16,9 +16,9 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from line_searching import *
-from conjugate_gradient import ConjugateGradient
-from descent_minimizer import DescentMinimizer
-from steepest_descent import SteepestDescent
-from vl_bfgs import VL_BFGS
-from relaxed_newton import RelaxedNewton
+from .line_searching import *
+from .conjugate_gradient import ConjugateGradient
+from .descent_minimizer import DescentMinimizer
+from .steepest_descent import SteepestDescent
+from .vl_bfgs import VL_BFGS
+from .relaxed_newton import RelaxedNewton

nifty/minimization/descent_minimizer.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.
 
+from __future__ import division
 import abc
 from nifty.nifty_meta import NiftyMeta
 
@@ -24,9 +25,10 @@ import numpy as np
 from keepers import Loggable
 
 from .line_searching import LineSearchStrongWolfe
+from future.utils import with_metaclass
 
 
-class DescentMinimizer(Loggable, object):
+class DescentMinimizer(with_metaclass(NiftyMeta, type('NewBase', (Loggable, object), {}))):
     """ A base class used by gradient methods to find a local minimum.
 
     Descent minimization methods are used to find a local minimum of a scalar
@@ -77,8 +79,6 @@ class DescentMinimizer(Loggable, object):
 
     """
 
-    __metaclass__ = NiftyMeta
-
     def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None,
                  convergence_tolerance=1E-4, convergence_level=3,
                  iteration_limit=None):

nifty/minimization/line_searching/__init__.py

@@ -16,5 +16,5 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from line_search import LineSearch
-from line_search_strong_wolfe import LineSearchStrongWolfe
+from .line_search import LineSearch
+from .line_search_strong_wolfe import LineSearchStrongWolfe

nifty/minimization/line_searching/line_search.py

@@ -21,9 +21,10 @@ import abc
 from keepers import Loggable
 
 from nifty import LineEnergy
+from future.utils import with_metaclass
 
 
-class LineSearch(Loggable, object):
+class LineSearch(with_metaclass(abc.ABCMeta, type('NewBase', (Loggable, object), {}))):
     """Class for determining the optimal step size along some descent direction.
     
     Initialize the line search procedure which can be used by a specific line
@@ -40,8 +41,6 @@ class LineSearch(Loggable, object):
         Initial guess for the step length.
     
     """
-    
-    __metaclass__ = abc.ABCMeta
 
     def __init__(self):
 

nifty/minimization/line_searching/line_search_strong_wolfe.py

@@ -16,6 +16,8 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
+from __future__ import division
+from builtins import range
 import numpy as np
 
 from .line_search import LineSearch
@@ -134,7 +136,7 @@ class LineSearchStrongWolfe(LineSearch):
         phiprime_alpha0 = phiprime_0
 
         # start the minimization loop
-        for i in xrange(max_iterations):
+        for i in range(max_iterations):
             energy_alpha1 = self.line_energy.at(alpha1)
             phi_alpha1 = energy_alpha1.value
             if alpha1 == 0:
@@ -243,7 +245,7 @@ class LineSearchStrongWolfe(LineSearch):
         alpha_recent = 0
         phi_recent = phi_0
 
-        for i in xrange(max_iterations):
+        for i in range(max_iterations):
             delta_alpha = alpha_hi - alpha_lo
             if delta_alpha < 0:
                 a, b = alpha_hi, alpha_lo