From fec4609f60e23aba665550cc588e3b98ec377d1f Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Wed, 14 Feb 2018 23:05:07 +0100
Subject: [PATCH] more doc work
---
nifty4/domains/power_space.py | 16 ++++++------
nifty4/library/critical_power_energy.py | 19 +++++++-------
nifty4/library/nonlinear_power_energy.py | 8 +++---
nifty4/minimization/conjugate_gradient.py | 4 +--
nifty4/minimization/descent_minimizer.py | 6 ++---
.../minimization/line_search_strong_wolfe.py | 18 ++++++-------
nifty4/minimization/minimizer.py | 5 ++--
nifty4/minimization/nonlinear_cg.py | 4 +--
nifty4/minimization/vl_bfgs.py | 12 ++++-----
nifty4/operators/laplace_operator.py | 2 +-
nifty4/operators/linear_operator.py | 26 +++++++++----------
nifty4/operators/smoothness_operator.py | 4 +--
12 files changed, 63 insertions(+), 61 deletions(-)
diff --git a/nifty4/domains/power_space.py b/nifty4/domains/power_space.py
index d4dc56ea0..1a0018019 100644
--- a/nifty4/domains/power_space.py
+++ b/nifty4/domains/power_space.py
@@ -24,14 +24,14 @@ from .. import dobj
class PowerSpace(StructuredDomain):
"""NIFTy class for spaces of power spectra.
- A power space is the result of a projection of a harmonic space where
+ A power space is the result of a projection of a harmonic domain where
k-modes of equal length get mapped to one power index.
Parameters
----------
- harmonic_partner : Space
- The harmonic Space of which this is the power space.
- binbounds: None, or tuple/array/list of float
+ harmonic_partner : StructuredDomain
+ The harmonic dmain of which this is the power space.
+ binbounds : None, or tuple of float
if None:
There will be as many bins as there are distinct k-vector lengths
in the harmonic partner space.
@@ -54,9 +54,9 @@ class PowerSpace(StructuredDomain):
binbounds[0]=first_bound and binbounds[-1]=last_bound and the remaining
values equidistantly spaced (in linear scale) between these two.
- nbin: integer
+ nbin : int
the number of bins
- first_bound, last_bound: float
+ first_bound, last_bound : float
the k values for the right boundary of the first bin and the left
boundary of the last bin, respectively. They are given in length
units of the harmonic partner space.
@@ -74,9 +74,9 @@ class PowerSpace(StructuredDomain):
values equidistantly spaced (in natural logarithmic scale)
between these two.
- nbin: integer
+ nbin : int
the number of bins
- first_bound, last_bound: float
+ first_bound, last_bound : float
the k values for the right boundary of the first bin and the left
boundary of the last bin, respectively. They are given in length
units of the harmonic partner space.
diff --git a/nifty4/library/critical_power_energy.py b/nifty4/library/critical_power_energy.py
index bc0807131..9dfa1180e 100644
--- a/nifty4/library/critical_power_energy.py
+++ b/nifty4/library/critical_power_energy.py
@@ -34,33 +34,34 @@ class CriticalPowerEnergy(Energy):
Parameters
----------
- position : Field,
+ position : Field
The current position of this energy. (Logarithm of power spectrum)
- m : Field,
+ m : Field
The map whose power spectrum is inferred. Needs to live in harmonic
signal space.
- D : EndomorphicOperator,
+ D : EndomorphicOperator, optional
The curvature of the Gaussian encoding the posterior covariance.
If not specified, the map is assumed to be no reconstruction.
default : None
- alpha : float
+ alpha : float, optional
The spectral prior of the inverse gamma distribution. 1.0 corresponds
to non-informative.
default : 1.0
- q : float
+ q : float, optional
The cutoff parameter of the inverse gamma distribution. 0.0 corresponds
to non-informative.
default : 0.0
- smoothness_prior : float
+ smoothness_prior : float, optional
Controls the strength of the smoothness prior
default : 0.0
- logarithmic : boolean
+ logarithmic : bool, optional
Whether smoothness acts on linear or logarithmic scale.
- samples : integer
+ default : True
+ samples : int, optional
Number of samples used for the estimation of the uncertainty
corrections.
default : 3
- w : Field
+ w : Field, optional
The contribution from the map with or without uncertainty. It is used
to pass on the result of the costly sampling during the minimization.
default : None
diff --git a/nifty4/library/nonlinear_power_energy.py b/nifty4/library/nonlinear_power_energy.py
index 670757d1f..602427475 100644
--- a/nifty4/library/nonlinear_power_energy.py
+++ b/nifty4/library/nonlinear_power_energy.py
@@ -34,18 +34,18 @@ class NonlinearPowerEnergy(Energy):
Parameters
----------
- position : Field,
+ position : Field
The current position of this energy.
- m : Field,
+ m : Field
The map whichs power spectrum has to be inferred
- D : EndomorphicOperator,
+ D : EndomorphicOperator
The curvature of the Gaussian encoding the posterior covariance.
If not specified, the map is assumed to be no reconstruction.
default : None
sigma : float
The parameter of the smoothness prior.
default : ??? None? ???????
- samples : integer
+ samples : int
Number of samples used for the estimation of the uncertainty
corrections.
default : 3
diff --git a/nifty4/minimization/conjugate_gradient.py b/nifty4/minimization/conjugate_gradient.py
index fad9d7333..974bb969e 100644
--- a/nifty4/minimization/conjugate_gradient.py
+++ b/nifty4/minimization/conjugate_gradient.py
@@ -56,9 +56,9 @@ class ConjugateGradient(Minimizer):
Returns
-------
- energy : QuadraticEnergy
+ QuadraticEnergy
state at last point of the iteration
- status : integer
+ int
Can be controller.CONVERGED or controller.ERROR
"""
controller = self._controller
diff --git a/nifty4/minimization/descent_minimizer.py b/nifty4/minimization/descent_minimizer.py
index a4e45b01c..05a142030 100644
--- a/nifty4/minimization/descent_minimizer.py
+++ b/nifty4/minimization/descent_minimizer.py
@@ -50,15 +50,15 @@ class DescentMinimizer(Minimizer):
Parameters
----------
- energy : Energy object
+ nergy : Energy
Energy object which provides value, gradient and curvature at a
specific position in parameter space.
Returns
-------
- energy : Energy object
+ Energy
Latest `energy` of the minimization.
- status : integer
+ int
Can be controller.CONVERGED or controller.ERROR
Notes
diff --git a/nifty4/minimization/line_search_strong_wolfe.py b/nifty4/minimization/line_search_strong_wolfe.py
index db87675c6..c53d8ad57 100644
--- a/nifty4/minimization/line_search_strong_wolfe.py
+++ b/nifty4/minimization/line_search_strong_wolfe.py
@@ -41,13 +41,13 @@ class LineSearchStrongWolfe(LineSearch):
Parameter for curvature condition rule. (Default: 0.9)
max_step_size : float
Maximum step allowed in to be made in the descent direction.
- (Default: 50)
- max_iterations : integer
+ (Default: 1000000000)
+ max_iterations : int, optional
Maximum number of iterations performed by the line search algorithm.
- (Default: 10)
- max_zoom_iterations : integer
+ (Default: 100)
+ max_zoom_iterations : int, optional
Maximum number of iterations performed by the zoom algorithm.
- (Default: 10)
+ (Default: 100)
"""
def __init__(self, c1=1e-4, c2=0.9,
@@ -71,18 +71,18 @@ class LineSearchStrongWolfe(LineSearch):
Parameters
----------
- energy : Energy object
+ energy : Energy
Energy object from which we will calculate the energy and the
gradient at a specific point.
pk : Field
Vector pointing into the search direction.
- f_k_minus_1 : float
+ f_k_minus_1 : float, optional
Value of the fuction (which is being minimized) at the k-1
iteration of the line search procedure. (Default: None)
Returns
-------
- energy_star : Energy object
+ Energy
The new Energy object on the new position.
"""
le_0 = LineEnergy(0., energy, pk, 0.)
@@ -188,7 +188,7 @@ class LineSearchStrongWolfe(LineSearch):
Returns
-------
- energy_star : Energy object
+ Energy
The new Energy object on the new position.
"""
cubic_delta = 0.2 # cubic interpolant checks
diff --git a/nifty4/minimization/minimizer.py b/nifty4/minimization/minimizer.py
index 1c6ff8b94..bbcee440e 100644
--- a/nifty4/minimization/minimizer.py
+++ b/nifty4/minimization/minimizer.py
@@ -39,8 +39,9 @@ class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
Returns
-------
- energy : Energy object
+ Energy
Latest `energy` of the minimization.
- status : integer
+ int
+ exit status of the minimization
"""
raise NotImplementedError
diff --git a/nifty4/minimization/nonlinear_cg.py b/nifty4/minimization/nonlinear_cg.py
index 32127e603..4b7146651 100644
--- a/nifty4/minimization/nonlinear_cg.py
+++ b/nifty4/minimization/nonlinear_cg.py
@@ -51,9 +51,9 @@ class NonlinearCG(Minimizer):
Returns
-------
- energy :
+ Energy
state at last point of the iteration
- status : integer
+ int
Can be controller.CONVERGED or controller.ERROR
"""
controller = self._controller
diff --git a/nifty4/minimization/vl_bfgs.py b/nifty4/minimization/vl_bfgs.py
index f9da3dd3d..e9239417a 100644
--- a/nifty4/minimization/vl_bfgs.py
+++ b/nifty4/minimization/vl_bfgs.py
@@ -87,7 +87,7 @@ class InformationStore(object):
Parameters
----------
- max_history_length : integer
+ max_history_length : int
Maximum number of stored past updates.
x0 : Field
Initial position in variable space.
@@ -96,7 +96,7 @@ class InformationStore(object):
Attributes
----------
- max_history_length : integer
+ max_history_length : int
Maximum number of stored past updates.
s : List
Circular buffer of past position differences, which are Fields.
@@ -106,7 +106,7 @@ class InformationStore(object):
Latest position in variable space.
last_gradient : Field
Gradient at latest position.
- k : integer
+ k : int
Number of updates that have taken place
ss : numpy.ndarray
2D circular buffer of scalar products between different elements of s.
@@ -139,7 +139,7 @@ class InformationStore(object):
Returns
-------
- result : List
+ List
List of new basis vectors.
"""
result = []
@@ -165,7 +165,7 @@ class InformationStore(object):
Returns
-------
- result : numpy.ndarray
+ numpy.ndarray
Scalar matrix.
"""
m = self.history_length
@@ -207,7 +207,7 @@ class InformationStore(object):
Returns
-------
- delta : List
+ List
List of the new scalar coefficients (deltas).
"""
m = self.history_length
diff --git a/nifty4/operators/laplace_operator.py b/nifty4/operators/laplace_operator.py
index 8b40a19fc..322e66434 100644
--- a/nifty4/operators/laplace_operator.py
+++ b/nifty4/operators/laplace_operator.py
@@ -35,7 +35,7 @@ class LaplaceOperator(EndomorphicOperator):
Parameters
----------
- logarithmic : boolean,
+ logarithmic : bool, optional
Whether smoothness is calculated on a logarithmic scale or linear scale
default : True
space : int
diff --git a/nifty4/operators/linear_operator.py b/nifty4/operators/linear_operator.py
index 96009d723..0993eb1ce 100644
--- a/nifty4/operators/linear_operator.py
+++ b/nifty4/operators/linear_operator.py
@@ -57,7 +57,7 @@ class LinearOperator(with_metaclass(
@abc.abstractproperty
def domain(self):
"""
- domain : DomainTuple
+ DomainTuple
The domain on which the Operator's input Field lives.
Every Operator which inherits from the abstract LinearOperator
base class must have this attribute.
@@ -67,7 +67,7 @@ class LinearOperator(with_metaclass(
@abc.abstractproperty
def target(self):
"""
- target : DomainTuple
+ DomainTuple
The domain on which the Operator's output Field lives.
Every Operator which inherits from the abstract LinearOperator
base class must have this attribute.
@@ -77,7 +77,7 @@ class LinearOperator(with_metaclass(
@property
def inverse(self):
"""
- inverse : LinearOperator
+ LinearOperator
Returns a LinearOperator object which behaves as if it were
the inverse of this operator.
"""
@@ -87,7 +87,7 @@ class LinearOperator(with_metaclass(
@property
def adjoint(self):
"""
- adjoint : LinearOperator
+ LinearOperator
Returns a LinearOperator object which behaves as if it were
the adjoint of this operator.
"""
@@ -141,7 +141,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : integer
+ int
This is any subset of LinearOperator.{TIMES, ADJOINT_TIMES,
INVERSE_TIMES, ADJOINT_INVERSE_TIMES, INVERSE_ADJOINT_TIMES},
joined together by the "|" operator.
@@ -158,7 +158,7 @@ class LinearOperator(with_metaclass(
The input Field, living on the Operator's domain or target,
depending on mode.
- mode : integer
+ mode : int
LinearOperator.TIMES: normal application
LinearOperator.ADJOINT_TIMES: adjoint application
LinearOperator.INVERSE_TIMES: inverse application
@@ -168,7 +168,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : Field
+ Field
The processed Field living on the Operator's target or domain,
depending on mode.
"""
@@ -188,7 +188,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : Field
+ Field
The processed Field living on the Operator's target domain.
"""
return self.apply(x, self.TIMES)
@@ -203,7 +203,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : Field
+ Field
The processed Field living on the Operator's domain.
"""
return self.apply(x, self.INVERSE_TIMES)
@@ -218,7 +218,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : Field
+ Field
The processed Field living on the Operator's domain.
"""
return self.apply(x, self.ADJOINT_TIMES)
@@ -233,7 +233,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- out : Field
+ Field
The processed Field living on the Operator's target domain.
Notes
@@ -267,7 +267,7 @@ class LinearOperator(with_metaclass(
Returns
-------
- sample : Field
- Returns the a sample from the Gaussian of given covariance.
+ Field
+ A sample from the Gaussian of given covariance.
"""
raise NotImplementedError
diff --git a/nifty4/operators/smoothness_operator.py b/nifty4/operators/smoothness_operator.py
index 682578f40..adb554ddb 100644
--- a/nifty4/operators/smoothness_operator.py
+++ b/nifty4/operators/smoothness_operator.py
@@ -36,9 +36,9 @@ def SmoothnessOperator(domain, strength=1., logarithmic=True, space=None):
Parameters
----------
- strength: nonnegative float
+ strength : nonnegative float
Specifies the strength of the SmoothnessOperator
- logarithmic : boolean
+ logarithmic : bool, optional
Whether smoothness is calculated on a logarithmic scale or linear scale
default : True
"""
--
GitLab