Commit 6ef6ce0c authored by Marco Selig's avatar Marco Selig

version update.

parent b934be46
......@@ -64,6 +64,8 @@ apply to fields.
* ``response_operator`` - exemplary responses that include a convolution,
masking and projection
* ``propagator_operator`` - information propagator in Wiener filter theory
* ``explicit_operator`` - linear operators with an explicit matrix
representation
* (and more)
* (and more)
......@@ -97,7 +99,7 @@ Requirements
Download
........
The latest release is tagged **v0.6.0** and is available as a source package
The latest release is tagged **v0.7.0** and is available as a source package
at `<https://github.com/mselig/nifty/tags>`_. The current version can be
obtained by cloning the repository::
......@@ -140,5 +142,7 @@ The NIFTY package is licensed under the
`GPLv3 <http://www.gnu.org/licenses/gpl.html>`_ and is distributed *without any
warranty*.
----
**NIFTY** project homepage: `<http://www.mpa-garching.mpg.de/ift/nifty/>`_
......@@ -24,6 +24,7 @@ from nifty_core import *
from nifty_cmaps import *
from nifty_power import *
from nifty_tools import *
from nifty_explicit import *
......
......@@ -63,15 +63,15 @@ j = R.adjoint_times(N.inverse_times(d)) # define i
class M_operator(operator):
def _multiply(self,x):
N,R = self.para
def _multiply(self, x):
N, R = self.para
return R.adjoint_times(N.inverse_times(R.times(x)))
C = explicify(S, newdomain=x_space, newtarget=x_space) # explicify S
M = M_operator(x_space,sym=True,uni=False,imp=True,para=(N,R))
M = M_operator(x_space, sym=True, uni=False, imp=True, para=(N, R))
M = explicify(M) # explicify M
D = (C.inverse()+M).inverse() # define information propagator
D = (C.inverse() + M).inverse() # define information propagator
m = D(j) # reconstruct map
......
......@@ -486,7 +486,7 @@ class _about(object): ## nifty support class for global settings
"""
## version
self._version = "0.6.8"
self._version = "0.7.0"
## switches and notifications
self._errors = notification(default=True,ccode=notification._code)
......@@ -8587,6 +8587,10 @@ class diagonal_operator(operator):
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
The inverse applications of the diagonal operator feature a ``pseudo``
flag indicating if zero divison shall be ignored and return zero
instead of causing an error.
Attributes
----------
domain : space
......@@ -9594,6 +9598,12 @@ class projection_operator(operator):
Lower limit of the uniform distribution if ``random == "uni"``
(default: 0).
Notes
-----
The application of the projection operator features a ``band`` keyword
specifying a single projection band (see examples) and a ``bandsup``
keyword specifying which projection bands to sum up.
Examples
--------
>>> space = point_space(3)
......
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......@@ -41,7 +41,7 @@
"""
from __future__ import division
from scipy.interpolate import interp1d as ip ## conflicts with sphinx's autodoc
from scipy.interpolate import interp1d as ip ## FIXME: conflicts with sphinx's autodoc
#import numpy as np
from nifty_core import *
import smoothing as gs
......
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