Merge branch 'sphinx_docs' into 'master'

Sphinx docs

See merge request !140

docs/howtogenerate.txt

+Two step creation of webpages:
+
+sphinx-apidoc -l -e -d 3 -o sphinx/source/mod/ nifty/ nifty/plotting/ nifty/spaces/power_space/power_indices.py nifty/spaces/power_space/power_index_factory.py nifty/config/ nifty/basic_arithmetics.py nifty/nifty_meta.py nifty/random.py nifty/version.py  nifty/field_types/ nifty/operators/fft_operator/transformations/rg_transforms.py
+
+
+creates all .rst files neccesary for ModuleIndex excluding helper modules
+
+sphinx-build -b html sphinx/source/ sphinx/build/
+
+generates html filel amd build directory

docs/source/_static/style.css

+/* override table width restrictions */
+
+.wy-nav-content {
+    max-width: none;
+}

docs/source/_templates/layout.html

+{% extends "!layout.html" %}
+{% block extrahead %}
+    <link href="{{ pathto("_static/style.css", True) }}" rel="stylesheet" type="text/css">
+{% endblock %}

docs/source/conf.py

@@ -16,7 +16,6 @@ from nifty import *
 import sys
 import os
 
-
 import sphinx_rtd_theme
 
 
@@ -89,9 +88,9 @@ author = u'Theo Steininger'
 # built documents.
 #
 # The short X.Y version.
-version = u'3.0.4'
+version = u'3.0'
 # The full version, including alpha/beta/rc tags.
-release = u'3.0.x'
+release = u'3.0.4'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -142,34 +141,20 @@ todo_include_todos = True
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-
 html_theme = "sphinx_rtd_theme"
 
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
 html_theme_options = {
-    'collapse_navigation': False,
-    'display_version': False,
-    'navigation_depth': 3,
+     'collapse_navigation': False,
+     'display_version': False,
+     'navigation_depth': 3,
 }
 
-#html_theme = 'alabaster'
-#html_theme_options = {
-#    'page_width':''
-#}
-
-#html_theme = 'classic'
-
-
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 
-
-
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
 # Add any paths that contain custom themes here, relative to this directory.
 #html_theme_path = []
 
@@ -215,7 +200,7 @@ html_last_updated_fmt = '%b %d, %Y'
 #html_additional_pages = {}
 
 # If false, no module index is generated.
-#html_domain_indices = True
+html_domain_indices = False
 
 # If false, no index is generated.
 #html_use_index = True
@@ -340,4 +325,4 @@ texinfo_documents = [
 
 
 # Example configuration for intersphinx: refer to the Python standard library.
-#intersphinx_mapping = {'https://docs.python.org/': None}
+#intersphinx_mapping = {'https://docs.python.org/': None}
\ No newline at end of file

docs/source/field.rst

@@ -10,8 +10,3 @@ In NIFTY, Fields are used to store data arrays and carry all the needed metainfo
 .. autoclass:: Field
     :show-inheritance:
     :members:
-
-    .. rubric:: Methods
-
-    .. autoautosummary:: Field
-        :methods:

docs/source/gallery.rst

+Image Gallery
+-------------
+
+Transformations & Projections
+.............................
+
+.. currentmodule:: nifty
+
+The "Faraday Map" [1]_ in spherical representation on a :py:class:`hp_space` and a :py:class:`gl_space`, their quadrupole projections, the uncertainty of the map, and the angular power spectrum.
+
++----------------------------+----------------------------+
+| .. image:: images/f_00.png | .. image:: images/f_01.png |
+|     :width:  90 %          |     :width:  90 %          |
++----------------------------+----------------------------+
+| .. image:: images/f_02.png | .. image:: images/f_03.png |
+|     :width:  90 %          |     :width:  90 %          |
++----------------------------+----------------------------+
+| .. image:: images/f_04.png | .. image:: images/f_05.png |
+|     :width:  90 %          |     :width:  70 %          |
++----------------------------+----------------------------+
+
+Gaussian random fields
+......................
+
+Statistically homogeneous and isotropic Gaussian random fields drawn from different power spectra.
+
++----------------------------+----------------------------+
+| .. image:: images/t_03.png | .. image:: images/t_04.png |
+|     :width:  60 %          |     :width:  70 %          |
++----------------------------+----------------------------+
+| .. image:: images/t_05.png | .. image:: images/t_06.png |
+|     :width:  60 %          |     :width:  70 %          |
++----------------------------+----------------------------+
+
+
+Wiener filtering I
+..................
+
+Wiener filter reconstruction of Gaussian random signal.
+
++--------------------------------+--------------------------------+--------------------------------+
+| original signal                | noisy data                     | reconstruction                 |
++================================+================================+================================+
+| .. image:: images/rg1_s.png    | .. image:: images/rg1_d.png    | .. image:: images/rg1_m.png    |
+|     :width:  90 %              |     :width:  90 %              |     :width:  90 %              |
++--------------------------------+--------------------------------+--------------------------------+
+| .. image:: images/rg2_s_pm.png | .. image:: images/rg2_d_pm.png | .. image:: images/rg2_m_pm.png |
+|     :width:  90 %              |     :width:  90 %              |     :width:  90 %              |
++--------------------------------+--------------------------------+--------------------------------+
+| .. image:: images/hp_s.png     | .. image:: images/hp_d.png     | .. image:: images/hp_m.png     |
+|     :width:  90 %              |     :width:  90 %              |     :width:  90 %              |
++--------------------------------+--------------------------------+--------------------------------+
+
+Image reconstruction
+....................
+
+Image reconstruction of the classic "Moon Surface" image. The original image "Moon Surface" was taken from the `USC-SIPI image database <http://sipi.usc.edu/database/>`_.
+
++-----------------------------------+-----------------------------------+-----------------------------------+
+| .. image:: images/moon_s.png      | .. image:: images/moon_d.png      | .. image:: images/moon_m.png      |
+|     :width:  90 %                 |     :width:  90 %                 |     :width:  90 %                 |
++-----------------------------------+-----------------------------------+-----------------------------------+
+| .. image:: images/moon_kernel.png | .. image:: images/moon_mask.png   | .. image:: images/moon_sigma.png  |
+|     :width:  90 %                 |     :width:  90 %                 |     :width:  90 %                 |
++-----------------------------------+-----------------------------------+-----------------------------------+
+
+Wiener filtering II
+...................
+
+Wiener filter reconstruction results for the full and partially blinded data. Shown are the original signal (orange), the reconstruction (green), and :math:`1\sigma`-confidence interval (gray).
+
++--------------------------------------+--------------------------------------+
+| noisy data                           | reconstruction results               |
++======================================+======================================+
+| .. image:: images/rg1_d.png          | .. image:: images/rg1_m_err_.png     |
+|     :width:  90 %                    |     :width:  90 %                    |
++--------------------------------------+--------------------------------------+
+| .. image:: images/rg1_d_gap.png      | .. image:: images/rg1_m_gap_err_.png |
+|     :width:  90 %                    |     :width:  90 %                    |
++--------------------------------------+--------------------------------------+
+
+D\ :sup:`3`\ PO -- Denoising, Deconvolving, and Decomposing Photon Observations
+...............................................................................
+
+Application of the D\ :sup:`3`\ PO algorithm [2]_ showing the raw photon count data and the denoised, deconvolved, and decomposed reconstruction of the diffuse photon flux.
+
++--------------------------------------+--------------------------------------+
+| .. image:: images/D3PO_data.png      | .. image:: images/D3PO_diffuse.png   |
+|     :width:  95 %                    |     :width:  95 %                    |
++--------------------------------------+--------------------------------------+
+
+RESOLVE -- Aperature synthesis imaging in radio astronomy
+.........................................................
+
+Signal inference on simulated single-frequency data: reconstruction by CLEAN (using uniform weighting) and by RESOLVE [3]_ (using IFT & NIFTY).
+
++-------------------------------------+-------------------------------------+-------------------------------------+
+| .. image:: images/radio_signal.png  | .. image:: images/radio_CLEAN.png   | .. image:: images/radio_RESOLVE.png |
+|     :width:  90 %                   |     :width:  90 %                   |     :width:  90 %                   |
++-------------------------------------+-------------------------------------+-------------------------------------+
+
+D\ :sup:`3`\ PO -- light
+........................
+
+Inference of the mock distribution of some species across Australia exploiting geospatial correlations in a (strongly) simplified scenario [4]_.
+
++--------------------------------+--------------------------------+--------------------------------+
+| .. image:: images/au_data.png  | .. image:: images/au_map.png   | .. image:: images/au_error.png |
+|     :width:  90 %              |     :width:  90 %              |     :width:  90 %              |
++--------------------------------+--------------------------------+--------------------------------+
+
+NIFTY meets Lensing
+...................
+
+Signal reconstruction for a simulated image that has undergone strong gravitational lensing. Without *a priori* knowledge of the signal covariance :math:`S`, a common approach rescaling the  `Laplace-Operator <http://de.wikipedia.org/wiki/Laplace-Operator>`_ and IFT's `"critical" filter <./demo_excaliwir.html#critical-wiener-filtering>`_ are compared.
+
++--------------------------------+--------------------------------+--------------------------------+--------------------------------+
+| .. image:: images/lens_s0.png  | .. image:: images/lens_d0.png  | .. image:: images/lens_m1.png  | .. image:: images/lens_m2.png  |
+|     :width:  80 %              |     :width:  80 %              |     :width:  80 %              |     :width:  80 %              |
+|                                |                                |                                |                                |
+|                                |                                | .. math::                      | .. math::                      |
+|                                |                                |     S(x,y) &=                  |     S(x,y) &=                  |
+|                                |                                |         \lambda \: \Delta^{-1} |         S(|x-y|)               |
+|                                |                                |         \\ \equiv              |         \\ \equiv              |
+|                                |                                |     S(k,l) &= \delta(k-l)      |     S(k,l) &= \delta(k-l)      |
+|                                |                                |         \: \lambda \: k^{-2}   |         \: P(k)                |
++--------------------------------+--------------------------------+--------------------------------+--------------------------------+
+
+.. [1] N. Oppermann et. al., "An improved map of the Galactic Faraday sky", Astronomy & Astrophysics, vol. 542, id. A93, p. 14, see also the `project homepage <http://www.mpa-garching.mpg.de/ift/faraday/>`_
+
+.. [2] M. Selig et. al., "Denoising, Deconvolving, and Decomposing Photon Observations", submitted to Astronomy & Astrophysics, 2013; `arXiv:1311.1888 <http://www.arxiv.org/abs/1311.1888>`_
+
+.. [3] H. Junklewitz et. al., "RESOLVE: A new algorithm for aperture synthesis imaging of extended emission in radio astronomy", submitted to Astronomy & Astrophysics, 2013; `arXiv:1311.5282 <http://www.arxiv.org/abs/1311.5282>`_
+
+.. [4] M. Selig, "The NIFTY way of Bayesian signal inference", submitted proceeding of the 33rd MaxEnt, 2013
+
+

docs/source/ift.rst

+IFT -- Information Field Theory
+===============================
+
+Theoretical Background
+----------------------
+
+
+`Information Field Theory <http://www.mpa-garching.mpg.de/ift/>`_ [1]_ (IFT) is information theory, the logic of reasoning under uncertainty, applied to fields. A field can be any quantity defined over some space, e.g. the air temperature over Europe, the magnetic field strength in the Milky Way, or the matter density in the Universe. IFT describes how data and knowledge can be used to infer field properties. Mathematically it is a statistical field theory and exploits many of the tools developed for such. Practically, it is a framework for signal processing and image reconstruction.
+
+IFT is fully Bayesian. How else can infinitely many field degrees of freedom be constrained by finite data?
+
+It can be used without the knowledge of Feynman diagrams. There is a full toolbox of methods. It reproduces many known well working algorithms. This should be reassuring. And, there were certainly previous works in a similar spirit. Anyhow, in many cases IFT provides novel rigorous ways to extract information from data.
+
+.. tip:: An *in-a-nutshell introduction to information field theory* can be found in [2]_.
+
+.. [1] T. Ensslin et al., "Information field theory for cosmological perturbation reconstruction and nonlinear signal analysis", PhysRevD.80.105005, 09/2009; `arXiv:0806.3474 <http://www.arxiv.org/abs/0806.3474>`_
+
+.. [2] T. Ensslin, "Information field theory", accepted for the proceedings of MaxEnt 2012 -- the 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering; `arXiv:1301.2556 <http://arxiv.org/abs/1301.2556>`_
+
+
+Discretized continuum
+---------------------
+
+The representation of fields that are mathematically defined on a continuous space in a finite computer environment is a common necessity. The goal hereby is to preserve the continuum limit in the calculus in order to ensure a resolution independent discretization.
+
++-----------------------------+-----------------------------+
+| .. image:: images/42vs6.png | .. image:: images/42vs9.png |
+|     :width:  100 %          |     :width:  100 %          |
++-----------------------------+-----------------------------+
+
+Any partition of the continuous position space :math:`\Omega` (with volume :math:`V`) into a set of :math:`Q` disjoint, proper subsets :math:`\Omega_q` (with volumes :math:`V_q`) defines a pixelization,
+
+.. math::
+
+    \Omega &\quad=\quad \dot{\bigcup_q} \; \Omega_q \qquad \mathrm{with} \qquad q \in \{1,\dots,Q\} \subset \mathbb{N}
+    , \\
+    V &\quad=\quad \int_\Omega \mathrm{d}x \quad=\quad \sum_{q=1}^Q \int_{\Omega_q} \mathrm{d}x \quad=\quad \sum_{q=1}^Q V_q
+    .
+
+Here the number :math:`Q` characterizes the resolution of the pixelization and the continuum limit is described by :math:`Q \rightarrow \infty` and :math:`V_q \rightarrow 0` for all :math:`q \in \{1,\dots,Q\}` simultaneously. Moreover, the above equation defines a discretization of continuous integrals, :math:`\int_\Omega \mathrm{d}x \mapsto \sum_q V_q`.
+
+Any valid discretization scheme for a field :math:`{s}` can be described by a mapping,
+
+.. math::
+
+    s(x \in \Omega_q) \quad\mapsto\quad s_q \quad=\quad \int_{\Omega_q} \mathrm{d}x \; w_q(x) \; s(x)
+    ,
+
+if the weighting function :math:`w_q(x)` is chosen appropriately. In order for the discretized version of the field to converge to the actual field in the continuum limit, the weighting functions need to be normalized in each subset; i.e., :math:`\forall q: \int_{\Omega_q} \mathrm{d}x \; w_q(x) = 1`. Choosing such a weighting function that is constant with respect to :math:`x` yields
+
+.. math::
+
+    s_q = \frac{\int_{\Omega_q} \mathrm{d}x \; s(x)}{\int_{\Omega_q} \mathrm{d}x} = \left< s(x) \right>_{\Omega_q}
+    ,
+
+which corresponds to a discretization of the field by spatial averaging. Another common and equally valid choice is :math:`w_q(x) = \delta(x-x_q)`, which distinguishes some position :math:`x_q \in \Omega_q`, and evaluates the continuous field at this position,
+
+.. math::
+
+    s_q \quad=\quad \int_{\Omega_q} \mathrm{d}x \; \delta(x-x_q) \; s(x) \quad=\quad s(x_q)
+    .
+
+In practice, one often makes use of the spatially averaged pixel position, :math:`x_q = \left< x \right>_{\Omega_q}`. If the resolution is high enough to resolve all features of the signal field :math:`{s}`, both of these discretization schemes approximate each other, :math:`\left< s(x) \right>_{\Omega_q} \approx s(\left< x \right>_{\Omega_q})`, since they approximate the continuum limit by construction. (The approximation of :math:`\left< s(x) \right>_{\Omega_q} \approx s(x_q \in \Omega_q)` marks a resolution threshold beyond which further refinement of the discretization reveals no new features; i.e., no new information content of the field :math:`{s}`.)
+
+All operations involving position integrals can be normalized in accordance with the above definitions. For example, the scalar product between two fields :math:`{s}` and :math:`{u}` is defined as
+
+.. math::
+
+    {s}^\dagger {u} \quad=\quad \int_\Omega \mathrm{d}x \; s^*(x) \; u(x) \quad\approx\quad \sum_{q=1}^Q V_q^{\phantom{*}} \; s_q^* \; u_q^{\phantom{*}}
+    ,
+
+where :math:`\dagger` denotes adjunction and :math:`*` complex conjugation. Since the above approximation becomes an equality in the continuum limit, the scalar product is independent of the pixelization scheme and resolution, if the latter is sufficiently high.
+
+The above line of argumentation analogously applies to the discretization of operators. For a linear operator :math:`{A}` acting on some field :math:`{s}` as :math:`{A} {s} = \int_\Omega \mathrm{d}y \; A(x,y) \; s(y)`, a matrix representation discretized with constant weighting functions is given by
+
+.. math::
+
+    A(x \in \Omega_p, y \in \Omega_q) \quad\mapsto\quad A_{pq} \quad=\quad \frac{\iint_{\Omega_p \Omega_q} \mathrm{d}x \, \mathrm{d}y \; A(x,y)}{\iint_{\Omega_p \Omega_q} \mathrm{d}x \, \mathrm{d}y} \quad=\quad \big< \big< A(x,y) \big>_{\Omega_p} \big>_{\Omega_q}
+    .
+
+The proper discretization of spaces, fields, and operators, as well as the normalization of position integrals, is essential for the conservation of the continuum limit. Their consistent implementation in NIFTY allows a pixelization independent coding of algorithms.
+
+