submodule lm created.

.gitignore

@@ -20,11 +20,11 @@ rg/*
 !rg/powerspectrum.py
 
 lm/*
-!rg/__init__.py
-!rg/nifty_lm.py
-!rg/nifty_gl.py
-!rg/nifty_hp.py
-!rg/nifty_power_conversion_lm.py
+!lm/__init__.py
+!lm/nifty_lm.py
+!lm/nifty_gl.py
+!lm/nifty_hp.py
+!lm/nifty_power_conversion_lm.py
 
 demos/*
 !demos/__init__.py

__init__.py

@@ -32,6 +32,12 @@ try:
 except(ImportError):
     pass
 
+## optional submodule `lm`
+try:
+    from lm import *
+except(ImportError):
+    pass
+
 from demos import *
 from pickling import *
 

lm/__init__.py

+## 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/>.
+
+from __future__ import division
+#from nifty_rg import *
+from nifty_power_conversion_lm import *
+

lm/nifty_power_conversion_lm.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/>.
+
+#from nifty import *
+import numpy as np
+from nifty import pi,                                                        \
+                  about,                                                     \
+                  field,                                                     \
+                  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
\ No newline at end of file

nifty_core.py

@@ -159,7 +159,7 @@ import libsharp_wrapper_gl as gl
 
 pi = 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679
 
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 
 
 ##-----------------------------------------------------------------------------