Commit a71f5b77 authored by Martin Reinecke's avatar Martin Reinecke

doc tweaks

parent 7f0d49de
Pipeline #24938 passed with stages
in 6 minutes and 36 seconds
......@@ -52,16 +52,12 @@ extensions = [
'numpydoc',
'sphinx.ext.autosummary',
'sphinx.ext.napoleon',
'sphinx.ext.coverage',
'sphinx.ext.todo',
# 'sphinx.ext.coverage',
# 'sphinx.ext.todo',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode'
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
......
NIFTy -- Numerical Information Field Theory
===========================================
**NIFTy** [1]_, "\ **N**\umerical **I**\nformation **F**\ield **T**\heor\ **y**\ ", is a versatile library designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution.
**NIFTy** [1]_, "\ **N**\umerical **I**\nformation **F**\ield **T**\heor\ **y**\ ", is a versatile library designed to enable the development of signal inference algorithms that are independent of the underlying spatial grid and its resolution.
Its object-oriented framework is written in Python, although it accesses libraries written in 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.
Thus, NIFTy permits its user to rapidly prototype algorithms in 1D and then apply the developed code in higher-dimensional settings to 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
----------
.. [1] 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>`_
Documentation
-------------
Welcome to NIFTy's documentation!
.. [1] Steininger et al., "NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters", 2017, submitted to PLOS One; `[arXiv:1708.01073] <https://arxiv.org/abs/1708.01073>`_
Contents
........
......
......@@ -2,7 +2,7 @@ Installation
============
In the following, we assume a Debian-based distribution. For other
In the following, we assume a Debian-based Linux distribution. For other
distributions, the "apt" lines will need slight changes.
NIFTy4 and its mandatory dependencies can be installed via::
......@@ -19,7 +19,7 @@ Plotting support is added via::
pip install --user matplotlib
Support for spherical harmonic transforms is added via:
Support for spherical harmonic transforms is added via::
pip install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
......
......@@ -24,34 +24,22 @@ from .. import dobj
class LMSpace(StructuredDomain):
"""NIFTy subclass for spherical harmonics components, for representations
of fields on the two-sphere.
"""NIFTy subclass for sets of spherical harmonic coefficients.
Its harmonic partner spaces are :class:`HPSpace` and :class:`GLSpace`.
Parameters
----------
lmax : int
The maximum :math:`l` value of any spherical harmonics
:math:`Y_{lm}` that is represented in this Space.
Must be >=0.
mmax : int *optional*
The maximum :math:`m` value of any spherical harmonics
:math:`Y_{lm}` that is represented in this Space.
If not supplied, it is set to lmax.
Must be >=0 and <=lmax.
See Also
--------
HPSpace, GLSpace
References
----------
.. [#] K.M. Gorski et al., 2005, "HEALPix: A Framework for
High-Resolution Discretization and Fast Analysis of Data
Distributed on the Sphere", *ApJ* 622..759G.
.. [#] M. Reinecke and D. Sverre Seljebotn, 2013, "Libsharp - spherical
harmonic transforms revisited";
`arXiv:1303.4945 <http://www.arxiv.org/abs/1303.4945>`_
The maximum :math:`l` value of any spherical harmonic coefficient
:math:`a_{lm}` that is represented by this object.
Must be :math:`\ge 0`.
mmax : int, optional
The maximum :math:`m` value of any spherical harmonic coefficient
:math:`a_{lm}` that is represented by this object.
If not supplied, it is set to `lmax`.
Must be :math:`\ge 0` and :math:`\le` `lmax`.
"""
def __init__(self, lmax, mmax=None):
......@@ -122,14 +110,14 @@ class LMSpace(StructuredDomain):
@property
def lmax(self):
""" Returns the maximum :math:`l` value of any spherical harmonic
:math:`Y_{lm}` that is represented in this Space.
coefficient :math:`a_{lm}` that is represented in this Space.
"""
return self._lmax
@property
def mmax(self):
""" Returns the maximum :math:`m` value of any spherical harmonic
:math:`Y_{lm}` that is represented in this Space.
coefficient :math:`a_{lm}` that is represented in this Space.
"""
return self._mmax
......
......@@ -35,7 +35,7 @@ class Field(object):
Parameters
----------
domain : None, DomainTuple, tuple(Domain), or Domain
domain : None, DomainTuple, tuple of Domain, or Domain
val : None, Field, data_object, or scalar
The values the array should contain after init. A scalar input will
......@@ -45,7 +45,7 @@ class Field(object):
dtype : type
A numpy.type. Most common are float and complex.
copy: boolean
copy: bool
"""
def __init__(self, domain=None, val=None, dtype=None, copy=False):
......@@ -143,7 +143,7 @@ class Field(object):
Parameters
----------
random_type : String
random_type : str
'pm1', 'normal', 'uniform' are the supported arguments for this
method.
......@@ -155,7 +155,7 @@ class Field(object):
Returns
-------
out : Field
Field
The output object.
"""
domain = DomainTuple.make(domain)
......@@ -187,7 +187,8 @@ class Field(object):
Returns
-------
Integer tuple containing the dimensions of the spaces in domain.
tuple of int
the dimensions of the spaces in domain.
"""
return self._domain.shape
......@@ -199,8 +200,8 @@ class Field(object):
Returns
-------
out : int
The dimension of the Field.
int
the dimension of the Field.
"""
return self._domain.size
......@@ -225,7 +226,7 @@ class Field(object):
Returns
-------
out : Field
Field
The output object. An identical copy of 'self'.
"""
return Field(val=self, copy=True)
......@@ -263,7 +264,7 @@ class Field(object):
power : number
The pixels get weighted with the volume-factor**power.
spaces : tuple of ints
spaces : int or tuple of int
Determines on which subspace the operation takes place.
out : Field or None
......@@ -273,7 +274,7 @@ class Field(object):
Returns
-------
out : Field
Field
The weighted field.
"""
if out is None:
......@@ -313,13 +314,13 @@ class Field(object):
x : Field
x must live on the same domain as `self`.
spaces : None, int or tuple of ints (default: None)
spaces : None, int or tuple of int (default: None)
The dot product is only carried out over the sub-domains in this
tuple. If None, it is carried out over all sub-domains.
Returns
-------
out : float, complex, either scalar (for full dot products)
float, complex, either scalar (for full dot products)
or Field (for partial dot products)
"""
if not isinstance(x, Field):
......@@ -343,7 +344,7 @@ class Field(object):
Returns
-------
norm : float
float
The L2-norm of the field values.
"""
return np.sqrt(np.abs(self.vdot(x=self)))
......@@ -353,7 +354,8 @@ class Field(object):
Returns
-------
The complex conjugated field.
Field
The complex conjugated field.
"""
return Field(self._domain, self.val.conjugate())
......
......@@ -39,18 +39,19 @@ class HarmonicTransformOperator(LinearOperator):
Parameters
----------
domain: Space, tuple of Spaces or DomainObject
domain : Domain, tuple of Domain or DomainTuple
The domain of the data that is input by "times" and output by
"adjoint_times".
target: Space (optional)
The target space of the transform operation.
If omitted, a space will be chosen automatically.
Whenever the input space of the transform is an RGSpace, the codomain
target : Domain, optional
The target domain of the transform operation.
If omitted, a domain will be chosen automatically.
Whenever the input domain of the transform is an RGSpace, the codomain
(and its parameters) are uniquely determined.
For LMSpace, a GLSpace of sufficient resolution is chosen.
space: the index of the space on which the operator should act
If None, it is set to 0 if domain contains exactly one space.
domain[space] must be a harmonic space.
space : int, optional
The index of the domain on which the operator should act
If None, it is set to 0 if domain contains exactly one subdomain.
domain[space] must be a harmonic domain.
"""
def __init__(self, domain, target=None, space=None):
......
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