Moved nifty source code into nifty subdirectory.

Adapted imports and setup.py to changes.

d2o_config

-[DEFAULT]
-mpi_module = MPI
-mpi_init_checks = False
-default_distribution_strategy = fftw
-default_comm = COMM_WORLD
-

__init__.py → nifty/__init__.py

@@ -39,11 +39,10 @@ from d2o import distributed_data_object, d2o_librarian
 
 from nifty_cmaps import ncmap
 from field import Field
-from space import Space
 
 # this line exists for compatibility reasons
 # TODO: Remove this once the transition to field types is done.
-from space import Space as point_space
+from spaces.space import Space as point_space
 
 from nifty_random import random
 from nifty_simple_math import *
@@ -56,15 +55,7 @@ from operators import *
 
 from spaces import *
 
-
 from demos import get_demo_dir
-from pickling import _pickle_method, _unpickle_method
 
 #import pyximport; pyximport.install(pyimport = True)
-from transformations import TransformationFactory,\
-                            GLLMTransformation,\
-                            HPLMTransformation,\
-                            LMGLTransformation,\
-                            LMHPTransformation,\
-                            RGRGTransformation
-
+from transformations import *
\ No newline at end of file

field.py → nifty/field.py

@@ -12,7 +12,7 @@ from nifty.config import about, \
 from nifty.field_types import FieldType,\
                               FieldArray
 
-from nifty.space import Space
+from nifty.spaces.space import Space
 
 import nifty.nifty_utilities as utilities
 from nifty_random import random

operators/nifty_operators.py → nifty/operators/nifty_operators.py

@@ -23,7 +23,7 @@ from __future__ import division
 import numpy as np
 from nifty.config import about
 from nifty.field import Field
-from nifty.space import Space
+from nifty.spaces.space import Space
 
 from nifty_minimization import conjugate_gradient
 from nifty_probing import trace_prober,\

operators/nifty_probing.py → nifty/operators/nifty_probing.py

@@ -22,7 +22,7 @@
 from __future__ import division
 
 from nifty.config import about
-from nifty.space import Space
+from nifty.spaces.space import Space
 from nifty.field import Field
 from nifty.nifty_utilities import direct_vdot
 

spaces/rg_space/nifty_power_conversion_rg.py → nifty/power_conversion.py

-# NIFTY (Numerical Information Field Theory) has been developed at the
-# Max-Planck-Institute for Astrophysics.
-#
-# Copyright (C) 2014 Max-Planck-Society
-#
-# Author: Maksim Greiner, Marco Selig
-# Project homepage: <http://www.mpa-garching.mpg.de/ift/nifty/>
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
-
+## NIFTY (Numerical Information Field Theory) has been developed at the
+## Max-Planck-Institute for Astrophysics.
+##
+## Copyright (C) 2015 Max-Planck-Society
+##
+## Author: Marco Selig
+## Project homepage: <http://www.mpa-garching.mpg.de/ift/nifty/>
+##
+## This program is free software: you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or
+## (at your option) any later version.
+##
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+## See the GNU General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with this program. If not, see <http://www.gnu.org/licenses/>.
 
 import numpy as np
+from numpy import pi
 from nifty.config import about
 from nifty.field import Field
+from nifty.nifty_simple_math import sqrt, exp, log
+
+
+def power_backward_conversion_lm(k_space, p, mean=None):
+    """
+        This function is designed to convert a theoretical/statistical power
+        spectrum of a log-normal field to the theoretical power spectrum of
+        the underlying Gaussian field.
+        The function only works for power spectra defined for lm_spaces
+
+        Parameters
+        ----------
+        k_space : nifty.rg_space,
+            a regular grid space with the attribute `Fourier = True`
+        p : np.array,
+            the power spectrum of the log-normal field.
+            Needs to have the same number of entries as
+            `k_space.get_power_indices()[0]`
+        mean : float, *optional*
+            specifies the mean of the log-normal field. If `mean` is not
+            specified the function will use the monopole of the power spectrum.
+            If it is specified the function will NOT use the monopole of the
+            spectrum. (default: None)
+            WARNING: a mean that is too low can violate positive definiteness
+            of the log-normal field. In this case the function produces an
+            error.
+
+        Returns
+        -------
+        mean : float,
+            the recovered mean of the underlying Gaussian distribution.
+        p1 : np.array,
+            the power spectrum of the underlying Gaussian field, where the
+            monopole has been set to zero. Eventual monopole power has been
+            shifted to the mean.
+
+        References
+        ----------
+        .. [#] M. Greiner and T.A. Ensslin, "Log-transforming the matter power spectrum";
+            `arXiv:1312.1354 <http://arxiv.org/abs/1312.1354>`_
+    """
+
+    p = np.copy(p)
+    if(mean is not None):
+        p[0] = 4*pi*mean**2
+
+    klen = k_space.get_power_indices()[0]
+    C_0_Omega = Field(k_space,val=0)
+    C_0_Omega.val[:len(klen)] = p*sqrt(2*klen+1)/sqrt(4*pi)
+    C_0_Omega = C_0_Omega.transform()
+
+    if(np.any(C_0_Omega.val<0.)):
+        raise ValueError(about._errors.cstring("ERROR: spectrum or mean incompatible with positive definiteness.\n Try increasing the mean."))
+        return None
+
+    lC = log(C_0_Omega)
+
+    Z = lC.transform()
+
+    spec = Z.val[:len(klen)]
+
+    mean = (spec[0]-0.5*sqrt(4*pi)*log((p*(2*klen+1)/(4*pi)).sum()))/sqrt(4*pi)
+
+    spec[0] = 0.
+
+    spec = spec*sqrt(4*pi)/sqrt(2*klen+1)
+
+    spec = np.real(spec)
+
+    if(np.any(spec<0.)):
+        spec = spec*(spec>0.)
+        about.warnings.cprint("WARNING: negative modes set to zero.")
+
+    return mean.real,spec
+
+
+def power_forward_conversion_lm(k_space,p,mean=0):
+    """
+        This function is designed to convert a theoretical/statistical power
+        spectrum of a Gaussian field to the theoretical power spectrum of
+        the exponentiated field.
+        The function only works for power spectra defined for lm_spaces
+
+        Parameters
+        ----------
+        k_space : nifty.rg_space,
+            a regular grid space with the attribute `Fourier = True`
+        p : np.array,
+            the power spectrum of the Gaussian field.
+            Needs to have the same number of entries as
+            `k_space.get_power_indices()[0]`
+        m : float, *optional*
+            specifies the mean of the Gaussian field (default: 0).
+
+        Returns
+        -------
+        p1 : np.array,
+            the power spectrum of the exponentiated Gaussian field.
+
+        References
+        ----------
+        .. [#] M. Greiner and T.A. Ensslin, "Log-transforming the matter power spectrum";
+            `arXiv:1312.1354 <http://arxiv.org/abs/1312.1354>`_
+    """
+    m = mean
+    klen = k_space.get_power_indices()[0]
+    C_0_Omega = Field(k_space,val=0)
+    C_0_Omega.val[:len(klen)] = p*sqrt(2*klen+1)/sqrt(4*pi)
+    C_0_Omega = C_0_Omega.transform()
+
+    C_0_0 = (p*(2*klen+1)/(4*pi)).sum()
+
+    exC = exp(C_0_Omega+C_0_0+2*m)
+
+    Z = exC.transform()
+
+    spec = Z.val[:len(klen)]
+
+    spec = spec*sqrt(4*pi)/sqrt(2*klen+1)
+
+    spec = np.real(spec)
+
+    if(np.any(spec<0.)):
+        spec = spec*(spec>0.)
+        about.warnings.cprint("WARNING: negative modes set to zero.")
+
+    return spec
 
 
 def power_backward_conversion_rg(k_space, p, mean=None, bare=True):
@@ -181,4 +308,4 @@ def power_forward_conversion_rg(k_space, p, mean=0, bare=True):
     if bare:
         new_spec /= weight
 
-    return new_spec.real
+    return new_spec.real
\ No newline at end of file

spaces/__init__.py → nifty/spaces/__init__.py

@@ -7,17 +7,13 @@ from hp_space import HPSpace,\
                      HPSpaceParadict
 
 from lm_space import LMSpace,\
-                     LMSpaceParadict,\
-                     power_backward_conversion_lm,\
-                     power_forward_conversion_lm
+                     LMSpaceParadict
 
 from power_space import PowerSpace,\
                         PowerSpaceParadict
 
 from rg_space import RGSpace,\
-                     RGSpaceParadict,\
-                     power_backward_conversion_rg,\
-                     power_forward_conversion_rg
+                     RGSpaceParadict
 
 from space import Space,\
                   SpaceParadict

spaces/gl_space/gl_space.py → nifty/spaces/gl_space/gl_space.py

@@ -7,9 +7,9 @@ from matplotlib.ticker import LogFormatter as lf
 
 from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
 
-from nifty.lm.lm_space import LMSpace
+from nifty.spaces.lm_space import LMSpace
 
-from nifty.space import Space
+from nifty.spaces.space import Space
 from nifty.config import about,\
                          nifty_configuration as gc,\
                          dependency_injector as gdi
@@ -570,6 +570,8 @@ class GLSpace(Space):
                 the figure is not saved (default: False).
 
         """
+        from nifty.field import Field
+
         try:
             x = x.get_full_data()
         except AttributeError:
@@ -602,11 +604,11 @@ class GLSpace(Space):
                 if(isinstance(other, tuple)):
                     other = list(other)
                     for ii in xrange(len(other)):
-                        if(isinstance(other[ii], field)):
+                        if(isinstance(other[ii], Field)):
                             other[ii] = other[ii].power(**kwargs)
                         else:
                             other[ii] = self.enforce_power(other[ii])
-                elif(isinstance(other, field)):
+                elif(isinstance(other, Field)):
                     other = [other.power(**kwargs)]
                 else:
                     other = [self.enforce_power(other)]

spaces/hp_space/hp_space.py → nifty/spaces/hp_space/hp_space.py

@@ -38,10 +38,9 @@ import pylab as pl
 
 from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
 
-from nifty.lm.lm_space import LMSpace
+from nifty.spaces.lm_space import LMSpace
 
-from nifty.space import Space
-from nifty.field import Field
+from nifty.spaces.space import Space
 
 from nifty.config import about,\
                          nifty_configuration as gc,\
@@ -523,6 +522,8 @@ class HPSpace(Space):
                 Number of iterations performed in the HEALPix basis
                 transformation.
         """
+        from nifty.field import Field
+
         try:
             x = x.get_full_data()
         except AttributeError:

spaces/lm_space/__init__.py → nifty/spaces/lm_space/__init__.py

@@ -3,6 +3,5 @@
 
 from lm_space import LMSpace
 from lm_space_paradict import LMSpaceParadict
-from nifty_power_conversion_lm import power_backward_conversion_lm,\
-                                      power_forward_conversion_lm
+
 

spaces/lm_space/lm_space.py → nifty/spaces/lm_space/lm_space.py

@@ -9,7 +9,7 @@ from matplotlib.ticker import LogFormatter as lf
 
 from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
 
-from nifty.space import Space
+from nifty.spaces.space import Space
 
 from nifty.config import about,\
                          nifty_configuration as gc,\
@@ -285,8 +285,8 @@ class LMSpace(Space):
         if codomain is None:
             return False
 
-        from hp_space import HPSpace
-        from gl_space import GLSpace
+        from nifty.spaces.hp_space import HPSpace
+        from nifty.spaces.gl_space import GLSpace
         if not isinstance(codomain, Space):
             raise TypeError(about._errors.cstring(
                 "ERROR: The given codomain must be a nifty lm_space."))
@@ -721,6 +721,8 @@ class LMSpace(Space):
                 the figure is not saved (default: False).
 
         """
+        from nifty.field import Field
+
         try:
             x = x.get_full_data()
         except AttributeError:
@@ -753,11 +755,11 @@ class LMSpace(Space):
                 if(isinstance(other, tuple)):
                     other = list(other)
                     for ii in xrange(len(other)):
-                        if(isinstance(other[ii], field)):
+                        if(isinstance(other[ii], Field)):
                             other[ii] = other[ii].power(**kwargs)
                         else:
                             other[ii] = self.enforce_power(other[ii])
-                elif(isinstance(other, field)):
+                elif(isinstance(other, Field)):
                     other = [other.power(**kwargs)]
                 else:
                     other = [self.enforce_power(other)]

spaces/power_space/power_space.py → nifty/spaces/power_space/power_space.py

@@ -4,7 +4,7 @@ import numpy as np
 from d2o import STRATEGIES
 
 from nifty.config import about
-from nifty.space import Space
+from nifty.spaces.space import Space
 from power_space_paradict import PowerSpaceParadict
 from nifty.nifty_utilities import cast_axis_to_tuple
 

spaces/rg_space/__init__.py → nifty/spaces/rg_space/__init__.py

@@ -21,6 +21,3 @@
 
 from rg_space import RGSpace
 from rg_space_paradict import RGSpaceParadict
-from nifty_power_conversion_rg import power_backward_conversion_rg,\
-                                      power_forward_conversion_rg
-

spaces/rg_space/rg_space.py → nifty/spaces/rg_space/rg_space.py

@@ -43,8 +43,7 @@ from matplotlib.ticker import LogFormatter as lf
 from d2o import distributed_data_object,\
                 STRATEGIES as DISTRIBUTION_STRATEGIES
 
-from nifty.space import Space
-from nifty.field import Field
+from nifty.spaces.space import Space
 
 from nifty.config import about,\
                          nifty_configuration as gc,\
@@ -397,6 +396,7 @@ class RGSpace(Space):
                 (default: 0).
 
         """
+        from nifty.field import Field
 
         if(not pl.isinteractive())and(not bool(kwargs.get("save",False))):
             about.warnings.cprint("WARNING: interactive mode off.")
@@ -453,11 +453,11 @@ class RGSpace(Space):
                 if(isinstance(other,tuple)):
                     other = list(other)
                     for ii in xrange(len(other)):
-                        if(isinstance(other[ii],field)):
+                        if(isinstance(other[ii],Field)):
                             other[ii] = other[ii].power(**kwargs)
                         else:
                             other[ii] = self.enforce_power(other[ii],size=np.size(xaxes),kindex=xaxes)
-                elif(isinstance(other,field)):
+                elif(isinstance(other,Field)):
                     other = [other.power(**kwargs)]
                 else:
                     other = [self.enforce_power(other,size=np.size(xaxes),kindex=xaxes)]
@@ -623,6 +623,8 @@ class RGSpace(Space):
                 Whether a scalar is extented to a constant array or not
                 (default: True).
         """
+        from nifty.field import Field
+
         about.warnings.cflush(
             "WARNING: _enforce_values is deprecated function. Please use self.cast")
         if(isinstance(x, Field)):

transformations/transformation_factory.py → nifty/transformations/transformation_factory.py

-from nifty.rg import RGSpace
-from nifty.lm import GLSpace, HPSpace, LMSpace
+from nifty.spaces import RGSpace, GLSpace, HPSpace, LMSpace
 
 from rgrgtransformation import RGRGTransformation
 from gllmtransformation import GLLMTransformation

nifty_config

-[DEFAULT]
-use_libsharp = True
-use_healpy = True
-verbosity = 1
-fft_module = pyfftw
-default_comm = COMM_WORLD
-mpi_module = MPI
-lm2gl = True
-

nifty_paradict.py

-# -*- coding: utf-8 -*-
-"""
-Created on Thu Apr  2 21:29:30 2015
-
-@author: steininger
-"""
-
-import numpy as np
-from nifty.config import about
-
-
-class space_paradict(object):
-
-    def __init__(self, **kwargs):
-        if not hasattr(self, 'parameters'):
-            self.parameters = {}
-        for key in kwargs:
-            self[key] = kwargs[key]
-
-    def __iter__(self):
-        return self.parameters.__iter__()
-
-    def __eq__(self, other):
-        return (isinstance(other, self.__class__) and
-                self.__dict__ == other.__dict__)
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __repr__(self):
-        return self.parameters.__repr__()
-
-    def __setitem__(self, key, arg):
-        raise NotImplementedError
-
-    def __getitem__(self, key):
-        return self.parameters.__getitem__(key)
-
-    def __hash__(self):
-        result_hash = 0
-        for (key, item) in self.parameters.items():
-            try:
-                temp_hash = hash(item)
-            except TypeError:
-                temp_hash = hash(tuple(item))
-            result_hash ^= temp_hash ^ int(hash(key)/131)
-        return result_hash
-
-
-class rg_space_paradict(space_paradict):
-
-    def __init__(self, shape, zerocenter, distances, harmonic):
-        if not hasattr(self, 'parameters'):
-            self.parameters = {}
-        self.parameters.__setitem__('shape', shape)
-        self.parameters.__setitem__('harmonic', harmonic)
-        space_paradict.__init__(
-            self, zerocenter=zerocenter, distances=distances)
-
-    def __setitem__(self, key, arg):
-        if key not in ['shape', 'zerocenter', 'distances']:
-            raise ValueError(about._errors.cstring(
-                "ERROR: Unsupported RGSpace parameter:" + key))
-
-        if key == 'shape':
-            temp = np.empty(len(self['shape']), dtype=np.int)
-            temp[:] = arg
-            temp = tuple(temp)
-        elif key == 'zerocenter':
-            temp = np.empty(len(