Skip to content
GitLab
Commit 0f817b6e authored by Reimar H Leike's avatar Reimar H Leike
Browse files

corrected the docstrings to respect inhereted docstrings and added docsgtrings... 

corrected the docstrings to respect inhereted docstrings and added docsgtrings for all methods where they were missing
parent addc84e3
Pipeline #12238 passed with stage
in 4 minutes and 39 seconds
Hide whitespace changes
Inline Side-by-side
nifty/domain_object.py
View file @ 0f817b6e
......@@ -131,11 +131,43 @@ class DomainObject(Versionable, Loggable, object):
"There is no generic weight-method for DomainObject.")
def pre_cast(self, x, axes=None):
# FIXME This does nothing and non of the children override this. Why does this exist?!
"""Casting operations that are done before fields are initialized.
Parameters
----------
x : {array-like, castable}
an array-like object or anything that can be cast to arrays. Examples are Floating Point Numbers
(which will get cast to a constant array) and functions (which will get evaluated on the field positions)
axes : {int, tuple}, *optional*
Specifies the axes of x which represent this domain.
(default: None).
If axes==None:
Assumes the axes to be the fields first axes
Returns
-------
{array-like, castable} : Processed input where casting that needs Space-specific knowledge (for example where points are) was performed.
Currently always returns x unless for a power spectrum is given to a PowerSpace, where this spectrum is evaluated at the power indices.
"""
return x
def post_cast(self, x, axes=None):
# FIXME This does nothing and non of the children override this. Why does this exist?!
"""Casting operations that are done after fields are initialized.
Parameters
----------
x : {array-like, castable}
an array-like object or anything that can be cast to arrays.
axes : {int, tuple}, *optional*
Specifies the axes of x which represent this domain.
(default: None).
If axes==None:
Assumes the axes to be the fields first axes
See Also
--------
pre_cast : Casting operations that are done before fields are initialized.
Returns
-------
array-like : Processed input where casting that needs Space-specific knowledge (for example where points are) was performed.
Currently always returns x unchanged.
"""
return x
# ---Serialization---
......
nifty/spaces/gl_space/gl_space.py
View file @ 0f817b6e
......@@ -98,12 +98,6 @@ class GLSpace(Space):
@property
def harmonic(self):
"""True if this can be regarded as a harmonic space.
Returns
-------
bool : False
Always returns False as the GLSpace cannot be regarded as harmonic space.
"""
return False
@property
......@@ -123,39 +117,6 @@ class GLSpace(Space):
nlon=self.nlon)
def weight(self, x, power=1, axes=None, inplace=False):
""" Weights a field living on this space with a specified amount of volume-weights.
Weights hereby refer to integration weights, as they appear in discretized integrals.
Per default, this function mutliplies each bin of the field x by its volume, which lets
it behave like a density (top form). However, different powers of the volume can be applied
with the power parameter. If only certain axes are specified via the axes parameter,
the weights are only applied with respect to these dimensions, yielding an object that
behaves like a lower degree form.
Parameters
----------
x : Field
A field with this space as domain to be weighted.
power : int, *optional*
The power to which the volume-weight is raised.
(default: 1).
axes : {int, tuple}, *optional*
Specifies for which axes the weights should be applied.
(default: None).
If axes==None:
weighting is applied with respect to all axes
inplace : bool, *optional*
If this is True, the weighting is done on the values of x,
if it is False, x is not modified and this method returns a
weighted copy of x
(default: False).
Returns
-------
Field
A weighted version of x, with volume-weights raised to power.
"""
nlon = self.nlon
nlat = self.nlat
vol = pyHealpix.GL_weights(nlat, nlon) ** power
......@@ -201,10 +162,20 @@ class GLSpace(Space):
@property
def nlat(self):
"""Number of latitudinal bins.
Returns
-------
int : Number of latitudinal bins (or rings) that are used in the pixelization
"""
return self._nlat
@property
def nlon(self):
"""Number of longditudinal bins.
Returns
-------
int : Number of longditudinal bins that are used in the pixelization
"""
return self._nlon
def _parse_nlat(self, nlat):
......
nifty/spaces/hp_space/hp_space.py
View file @ 0f817b6e
......@@ -102,42 +102,9 @@ class HPSpace(Space):
return 4 * np.pi
def copy(self):
"""Returns a copied version of this HPSpace.
Returns
-------
HPSpace : A copy of this object.
"""
return self.__class__(nside=self.nside)
def weight(self, x, power=1, axes=None, inplace=False):
""" Weights a field living on this space with a specified amount of volume-weights.
Weights hereby refer to integration weights, as they appear in discretized integrals.
Per default, this function mutliplies each bin of the field x by its volume, which lets
it behave like a density (top form). However, different powers of the volume can be applied
with the power parameter.
Parameters
----------
x : Field
A field with this space as domain to be weighted.
power : int, *optional*
The power to which the volume-weight is raised.
(default: 1).
axes : {int, tuple}, *optional*
This should not be used. It does nothing.
inplace : bool, *optional*
If this is True, the weighting is done on the values of x,
if it is False, x is not modified and this method returns a
weighted copy of x
(default: False).
Returns
-------
Field
A weighted version of x, with volume-weights raised to power.
"""
weight = ((4 * np.pi) / (12 * self.nside**2))**power
......@@ -150,29 +117,20 @@ class HPSpace(Space):
return result_x
def get_distance_array(self, distribution_strategy):
"""This should not be used, it just raises an error when called.
Raises
------
NotImplementedError
Always when called.
"""
raise NotImplementedError
def get_fft_smoothing_kernel_function(self, sigma):
"""This should not be used, it just raises an error when called.
Raises
------
NotImplementedError
Always when called.
"""
raise NotImplementedError
# ---Added properties and methods---
@property
def nside(self):
"""Returns the nside of the corresponding HEALPix pixelization.
Returns
-------
int : the nside of the corresponding HEALPix pixelization. The total number of pixels is 12*nside**2
"""
return self._nside
def _parse_nside(self, nside):
......
nifty/spaces/lm_space/lm_space.py
View file @ 0f817b6e
......@@ -124,65 +124,15 @@ class LMSpace(Space):
return np.float64(self.dim)
def copy(self):
"""Returns a copied version of this LMSpace.
Returns
-------
LMSpace : A copy of this object.
"""
return self.__class__(lmax=self.lmax)
def weight(self, x, power=1, axes=None, inplace=False):
""" Weights a field living on this space with a specified amount of volume-weights.
Weights hereby refer to integration weights, as they appear in discretized integrals.
Per default, this function mutliplies each bin of the field x by its volume, which lets
it behave like a density (top form). All volume-weights are 1, thus nothing happens.
Parameters
----------
x : Field
A field with this space as domain to be weighted.
power : int, *optional*
The power to which the volume-weight is raised. It does nothing.
(default: 1).
axes : {int, tuple}, *optional*
This should not be used. It does nothing.
inplace : bool, *optional*
If this is True, the weighting is done on the values of x,
if it is False, x is not modified and this method returns a
weighted copy of x
(default: False).
Returns
-------
Field
x or a copy of x.
"""
if inplace:
return x
else:
return x.copy()
def get_distance_array(self, distribution_strategy):
"""Returns the distance of the bins to zero.
Calculates an 2-dimensional array with its entries being the
lengths of the k-vectors from the zero point of the grid.
Parameters
----------
distribution_strategy :
Returns
-------
nkdict : distributed_data_object
Raises
------
ValueError
The distribution_strategy is neither slicing nor not.
"""
dists = arange(start=0, stop=self.shape[0],
distribution_strategy=distribution_strategy)
......@@ -208,10 +158,24 @@ class LMSpace(Space):
@property
def lmax(self):
"""Returns the maximal :math:`l`-value of the spherical harmonics being used.
Returns
-------
int : maximal :math:`l` value of any spherical harmonic :math:`Y_{lm}` that is represented in this Space.
"""
return self._lmax
@property
def mmax(self):
"""Returns the maximal :math:`m`-value of the spherical harmonics being used.
See Also
--------
lmax : Returns the maximal :math:`l`-value of the spherical harmonics being used.
Returns
-------
int : maximal :math:`m` value of any spherical harmonic :math:`Y_{lm}` that is represented in this Space.
As :math:`m` goes from :math:`-l` to :math:`l` for every :math:`l` this just returns the same as lmax.
"""
return self._lmax
def _parse_lmax(self, lmax):
......
nifty/spaces/rg_space/rg_space.py
View file @ 0f817b6e
......@@ -101,24 +101,6 @@ class RGSpace(Space):
def hermitian_decomposition(self, x, axes=None,
preserve_gaussian_variance=False):
"""Separates the hermitian and antihermitian part of a field.
This is a function which is called by the field in order to separate itself for
each of its domains.
Parameters
----------
x: Field
Field to be decomposed.
axes: {int, tuple}, *optional*
Specifies which indices of the field belongs to this RGSpace. If None, it
takes the first dimensions of the field.
(default: None)
preserve_gaussian_variance: bool, *optional*
(default: False)
"""
# compute the hermitian part
flipped_x = self._hermitianize_inverter(x, axes=axes)
flipped_x = flipped_x.conjugate()
......@@ -208,50 +190,12 @@ class RGSpace(Space):
return self.dim * reduce(lambda x, y: x*y, self.distances)
def copy(self):
"""Returns a copied version of this RGSpace.
Returns
-------
RGSpace : A copy of this object.
"""
return self.__class__(shape=self.shape,
zerocenter=self.zerocenter,
distances=self.distances,
harmonic=self.harmonic)
def weight(self, x, power=1, axes=None, inplace=False):
""" Weights a field living on this space with a specified amount of volume-weights.
Weights hereby refer to integration weights, as they appear in discretized integrals.
Per default, this function mutliplies each bin of the field x by its volume, which lets
it behave like a density (top form). However, different powers of the volume can be applied
with the power parameter. If only certain axes are specified via the axes parameter,
the weights are only applied with respect to these dimensions, yielding an object that
behaves like a lower degree form.
Parameters
----------
x : Field
A field with this space as domain to be weighted.
power : int, *optional*
The power to which the volume-weight is raised.
(default: 1).
axes : {int, tuple}, *optional*
Specifies for which axes the weights should be applied.
(default: None).
If axes==None:
weighting is applied with respect to all axes
inplace : bool, *optional*
If this is True, the weighting is done on the values of x,
if it is False, x is not modified and this method returns a
weighted copy of x
(default: False).
Returns
-------
Field
A weighted version of x, with volume-weights raised to power.
"""
weight = reduce(lambda x, y: x*y, self.distances)**power
if inplace:
x *= weight
......@@ -261,26 +205,6 @@ class RGSpace(Space):
return result_x
def get_distance_array(self, distribution_strategy):
"""Returns the distance of the bins to zero.
Calculates an n-dimensional array with its entries being the
lengths of the k-vectors from the zero point of the grid.
MR FIXME: Since this is about k-vectors, it might make sense to
throw NotImplementedError if harmonic==False.
Parameters
----------
None : All information is taken from the parent object.
Returns
-------
nkdict : distributed_data_object
Raises
------
ValueError
The distribution_strategy is neither slicing nor not.
"""
shape = self.shape
# prepare the distributed_data_object
nkdict = distributed_data_object(
......@@ -336,10 +260,23 @@ class RGSpace(Space):
@property
def distances(self):
"""Returns the distance of gridpoints along each axis
Returns
-------
array-like : An array of positive floating point numbers with the
n-th entry giving the distances of grid points along the n-th dimension.
"""
return self._distances
@property
def zerocenter(self):
"""Returns True if grid points lie symmetrically around zero
Returns
-------
bool : True if the grid points are centered around the 0 grid point. This option
makes most sense for harmonic space (where the zero-mode plays a special role)
but may be used for position space, too.
"""
return self._zerocenter
def _parse_shape(self, shape):
......
nifty/spaces/space/space.py
View file @ 0f817b6e
......@@ -160,6 +160,16 @@ class Space(DomainObject):
@abc.abstractproperty
def harmonic(self):
"""Returns True if this Space can be regarded as a harmonic space.
Returns
-------
bool : True if can be regarded as a harmonic space to another space. False otherwise
Raises
------
NotImplementedError : If it is called for an abstract class, all non-abstract child-classes should
implement this.
"""
raise NotImplementedError
@abc.abstractproperty
......@@ -247,7 +257,7 @@ class Space(DomainObject):
Specifies the axes of x which represent this domain.
(default: None).
If axes==None:
weighting is applied with respect to all axes
Assumes the axes to be the fields first axes
preserve_gaussian_variance : bool *optional*
FIXME: figure out what this does
Returns
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment