added a studentTOperator analogously to the InverseGammaOperator. Added tests...

added a studentTOperator analogously to the InverseGammaOperator. Added tests and raised the jacobian tolerance for both Operators. Fixed bugs from previious commit

nifty5/__init__.py

@@ -74,6 +74,7 @@ from .sugar import *
 from .plot import Plot
 
 from .library.inverse_gamma_operator import InverseGammaOperator
+from .library.studentt_operator import StudentTOperator
 from .library.los_response import LOSResponse
 from .library.dynamic_operator import (dynamic_operator,
                                        dynamic_lightcone_operator)

nifty5/library/studentt_operator.py

+# 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/>.
+#
+# Copyright(C) 2013-2019 Max-Planck-Society
+#
+# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
+
+import numpy as np
+from scipy.stats import t, norm
+
+from ..operators.interpolating_operator import InterpolatingOperator
+
+
+def StudentTOperator(domain, alpha, q, bounds=(-8.2,8.2), delta=0.001):
+    """Transforms a Gaussian into a Student T distributed quantity
+
+    The pdf of the Student T for :math:`q=1` is defined as follows:
+
+    .. math::
+        \\frac{\\Gamma(\\frac{\\alpha+1}{2})}
+        {\\sqrt{\\alpha \\pi}\\Gamma(\\frac{\\alpha}{2})}
+        (1+\\frac{x^2}{\\alpha})^{-\\frac{\\alpha+1}{2}}
+
+    The parameter :math:`q` is just a scaling parameter
+    The mean and mode of the distribution is at 0
+    The variance is "math:`\\frac{q^2\\alpha}{\\alpha-2}` for :math:`\\alpha>2`
+
+    This transformation is implemented as a linear interpolation which maps a
+    Gaussian onto a Student T distribution
+
+    Parameters
+    ----------
+    domain : Domain, tuple of Domain or DomainTuple
+        The domain on which the field shall be defined. This is at the same
+        time the domain and the target of the operator.
+    alpha : float
+        The degree of freedom parameter of the Student T distribution
+    q : float
+        The scale parameter of the Student T distribution
+    delta : float
+        distance between sampling points for linear interpolation.
+    """
+    func = lambda x: t.ppf(norm.cdf(x), alpha)
+    return InterpolatingOperator(domain, func, bounds, delta)

test/test_operators/test_jacobian.py

@@ -96,7 +96,9 @@ def testPointModel(space, seed):
     q = 0.73
     model = ift.InverseGammaOperator(space, alpha, q)
     # FIXME All those cdfs and ppfs are not very accurate
-    ift.extra.check_jacobian_consistency(model, pos, tol=1e-2, ntries=20)
+    ift.extra.check_jacobian_consistency(model, pos, tol=1e-5, ntries=20)
+    model = ift.StudentTOperator(space, alpha, q)
+    ift.extra.check_jacobian_consistency(model, pos, tol=1e-5, ntries=20)
 
 
 @pmp('target', [