Commit 26eb1cae authored by Theo Steininger's avatar Theo Steininger
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Updated docstring of Space class.

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......@@ -16,127 +16,7 @@
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
.. __ ____ __
.. /__/ / _/ / /_
.. __ ___ __ / /_ / _/ __ __
.. / _ | / / / _/ / / / / / /
.. / / / / / / / / / /_ / /_/ /
.. /__/ /__/ /__/ /__/ \___/ \___ / core
.. /______/
.. The NIFTY project homepage is http://www.mpa-garching.mpg.de/ift/nifty/
NIFTY [#]_, "Numerical Information Field Theory", is a versatile
library designed to enable the development of signal inference algorithms
that operate regardless of the underlying spatial grid and its resolution.
Its object-oriented framework is written in Python, although it accesses
libraries written in Cython, C++, and C for efficiency.
NIFTY offers a toolkit that abstracts discretized representations of
continuous spaces, fields in these spaces, and operators acting on fields
into classes. Thereby, the correct normalization of operations on fields is
taken care of automatically without concerning the user. This allows for an
abstract formulation and programming of inference algorithms, including
those derived within information field theory. Thus, NIFTY permits its user
to rapidly prototype algorithms in 1D and then apply the developed code in
higher-dimensional settings of real world problems. The set of spaces on
which NIFTY operates comprises point sets, n-dimensional regular grids,
spherical spaces, their harmonic counterparts, and product spaces
constructed as combinations of those.
References
----------
.. [#] Selig et al., "NIFTY -- Numerical Information Field Theory --
a versatile Python library for signal inference",
`A&A, vol. 554, id. A26 <http://dx.doi.org/10.1051/0004-6361/201321236>`_,
2013; `arXiv:1301.4499 <http://www.arxiv.org/abs/1301.4499>`_
Class & Feature Overview
------------------------
The NIFTY library features three main classes: **spaces** that represent
certain grids, **fields** that are defined on spaces, and **operators**
that apply to fields.
.. Overview of all (core) classes:
..
.. - switch
.. - notification
.. - _about
.. - random
.. - space
.. - point_space
.. - rg_space
.. - lm_space
.. - gl_space
.. - hp_space
.. - nested_space
.. - field
.. - operator
.. - diagonal_operator
.. - power_operator
.. - projection_operator
.. - vecvec_operator
.. - response_operator
.. - probing
.. - trace_probing
.. - diagonal_probing
Overview of the main classes and functions:
.. automodule:: nifty
- :py:class:`space`
- :py:class:`point_space`
- :py:class:`rg_space`
- :py:class:`lm_space`
- :py:class:`gl_space`
- :py:class:`hp_space`
- :py:class:`nested_space`
- :py:class:`field`
- :py:class:`operator`
- :py:class:`diagonal_operator`
- :py:class:`power_operator`
- :py:class:`projection_operator`
- :py:class:`vecvec_operator`
- :py:class:`response_operator`
.. currentmodule:: nifty.nifty_tools
- :py:class:`invertible_operator`
- :py:class:`propagator_operator`
.. currentmodule:: nifty.nifty_explicit
- :py:class:`explicit_operator`
.. automodule:: nifty
- :py:class:`probing`
- :py:class:`trace_probing`
- :py:class:`diagonal_probing`
.. currentmodule:: nifty.nifty_explicit
- :py:class:`explicit_probing`
.. currentmodule:: nifty.nifty_tools
- :py:class:`conjugate_gradient`
- :py:class:`steepest_descent`
.. currentmodule:: nifty.nifty_explicit
- :py:func:`explicify`
.. currentmodule:: nifty.nifty_power
- :py:func:`weight_power`,
:py:func:`smooth_power`,
:py:func:`infer_power`,
:py:func:`interpolate_power`
"""
from __future__ import division
import abc
......@@ -145,17 +25,43 @@ from nifty.domain_object import DomainObject
class Space(DomainObject):
def __init__(self):
"""
"""The abstract base class for all NIFTy spaces.
An instance of a space contains information about the manifolds geometry
and enhances the functionality of DomainObject by methods that are needed
for powerspectrum analysis and smoothing.
Parameters
----------
None.
None
Returns
-------
None.
Attributes
----------
dim : np.int
Total number of dimensionality, i.e. the number of pixels.
harmonic : bool
Specifies whether the space is a signal or harmonic space.
total_volume : np.float
The total volume of the space.
shape : tuple of np.ints
The shape of the space's data array.
Raises
------
TypeError
Raised if instantiated directly.
Notes
-----
`Space` is an abstract base class. In order to allow for instantiation the
methods `get_distance_array`, `total_volume` and `copy` must be implemented
as well as the abstract methods inherited from `DomainObject`.
See Also
--------
distributor
"""
def __init__(self):
super(Space, self).__init__()
@abc.abstractproperty
......
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