# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module provides a class to fit elliptical isophotes.
"""
import warnings
import numpy as np
from astropy.utils.exceptions import AstropyUserWarning
from photutils.isophote.fitter import (DEFAULT_CONVERGENCE, DEFAULT_FFLAG,
DEFAULT_MAXGERR, DEFAULT_MAXIT,
DEFAULT_MINIT, CentralEllipseFitter,
EllipseFitter)
from photutils.isophote.geometry import EllipseGeometry
from photutils.isophote.integrator import BILINEAR
from photutils.isophote.isophote import Isophote, IsophoteList
from photutils.isophote.sample import CentralEllipseSample, EllipseSample
__all__ = ['Ellipse']
[docs]
class Ellipse:
r"""
Class to fit elliptical isophotes to a galaxy image.
The isophotes in the image are measured using an iterative method
described by `Jedrzejewski (1987; MNRAS 226, 747)
<https://ui.adsabs.harvard.edu/abs/1987MNRAS.226..747J/abstract>`_.
See the **Notes** section below for details about the algorithm.
Parameters
----------
image : 2D `~numpy.ndarray`
The image array.
geometry : `~photutils.isophote.EllipseGeometry` instance or `None`, optional
The optional geometry that describes the first ellipse to be
fitted. If `None`, a default
`~photutils.isophote.EllipseGeometry` instance is created
centered on the image frame with ellipticity of 0.2 and a
position angle of 90 degrees.
threshold : float, optional
The threshold for the object centerer algorithm. By lowering
this value the object centerer becomes less strict, in the sense
that it will accept lower signal-to-noise data. If set to a very
large value, the centerer is effectively shut off. In this case,
either the geometry information supplied by the ``geometry``
parameter is used as is, or the fit algorithm will terminate
prematurely. Note that once the object centerer runs
successfully, the (x, y) coordinates in the ``geometry``
attribute (an `~photutils.isophote.EllipseGeometry` instance)
are modified in place. The default is 0.1.
Notes
-----
The image is measured using an iterative method described by
`Jedrzejewski (1987; MNRAS 226, 747)
<https://ui.adsabs.harvard.edu/abs/1987MNRAS.226..747J/abstract>`_.
Each isophote is fitted at a pre-defined, fixed semimajor axis
length. The algorithm starts from a first-guess elliptical isophote
defined by approximate values for the (x, y) center coordinates,
ellipticity, and position angle. Using these values, the image is
sampled along an elliptical path, producing a 1-dimensional function
that describes the dependence of intensity (pixel value) with angle
(E). The function is stored as a set of 1D numpy arrays. The
harmonic content of this function is analyzed by least-squares
fitting to the function:
.. math::
y = y0 + (A1 * \sin(E)) + (B1 * \cos(E)) + (A2 * \sin(2 * E))
+ (B2 * \cos(2 * E))
Each one of the harmonic amplitudes (A1, B1, A2, and B2) is related
to a specific ellipse geometric parameter in the sense that it
conveys information regarding how much the parameter's current value
deviates from the "true" one. To compute this deviation, the image's
local radial gradient has to be taken into account too. The
algorithm picks up the largest amplitude among the four, estimates
the local gradient, and computes the corresponding increment in the
associated ellipse parameter. That parameter is updated, and the
image is resampled. This process is repeated until any one of the
following criteria are met:
1. the largest harmonic amplitude is less than a given fraction of
the rms residual of the intensity data around the harmonic fit.
2. a user-specified maximum number of iterations is reached.
3. more than a given fraction of the elliptical sample points have no
valid data in then, either because they lie outside the image
boundaries or because they were flagged out from the fit by
sigma-clipping.
In any case, a minimum number of iterations is always performed. If
iterations stop because of reasons 2 or 3 above, then those ellipse
parameters that generated the lowest absolute values for harmonic
amplitudes will be used. At this point, the image data sample coming
from the best fit ellipse is fitted by the following function:
.. math::
y = y0 + (An * sin(n * E)) + (Bn * cos(n * E))
with :math:`n = 3` and :math:`n = 4`. The corresponding amplitudes
(A3, B3, A4, and B4), divided by the semimajor axis length and local
intensity gradient, measure the isophote's deviations from perfect
ellipticity (these amplitudes, divided by semimajor axis and
gradient, are the actual quantities stored in the output
`~photutils.isophote.Isophote` instance).
The algorithm then measures the integrated intensity and the number
of non-flagged pixels inside the elliptical isophote, and also
inside the corresponding circle with same center and radius equal to
the semimajor axis length. These parameters, their errors, other
associated parameters, and auxiliary information, are stored in the
`~photutils.isophote.Isophote` instance.
Errors in intensity and local gradient are obtained directly from
the rms scatter of intensity data along the fitted ellipse. Ellipse
geometry errors are obtained from the errors in the coefficients of
the first and second simultaneous harmonic fit. Third and fourth
harmonic amplitude errors are obtained in the same way, but only
after the first and second harmonics are subtracted from the raw
data. For more details, see the error analysis in `Busko (1996;
ASPC 101, 139)
<https://ui.adsabs.harvard.edu/abs/1996ASPC..101..139B/abstract>`_.
After fitting the ellipse that corresponds to a given value of the
semimajor axis (by the process described above), the axis length is
incremented/decremented following a pre-defined rule. At each step,
the starting, first-guess, ellipse parameters are taken from the
previously fitted ellipse that has the closest semimajor axis length
to the current one. On low surface brightness regions (those having
large radii), the small values of the image radial gradient can
induce large corrections and meaningless values for the ellipse
parameters. The algorithm has the ability to stop increasing
semimajor axis based on several criteria, including signal-to-noise
ratio.
See the `~photutils.isophote.Isophote` documentation for the meaning
of the stop code reported after each fit.
The fit algorithm provides a k-sigma clipping algorithm for cleaning
deviant sample points at each isophote, thus improving convergence
stability against any non-elliptical structure such as stars, spiral
arms, HII regions, defects, etc.
The fit algorithm has no way of finding where, in the input image
frame, the galaxy to be measured is located. The center (x, y)
coordinates need to be close to the actual center for the fit to
work. An "object centerer" function helps to verify that the
selected position can be used as starting point. This function scans
a 10x10 window centered either on the (x, y) coordinates in the
`~photutils.isophote.EllipseGeometry` instance passed to the
constructor of the `~photutils.isophote.Ellipse` class, or, if any
one of them, or both, are set to `None`, on the input image frame
center. In case a successful acquisition takes place, the
`~photutils.isophote.EllipseGeometry` instance is modified in place
to reflect the solution of the object centerer algorithm.
In some cases the object centerer algorithm may fail, even though
there is enough signal-to-noise to start a fit (e.g., in objects
with very high ellipticity). In those cases the sensitivity of the
algorithm can be decreased by decreasing the value of the object
centerer threshold parameter. The centerer works by looking to where
a quantity akin to a signal-to-noise ratio is maximized within the
10x10 window. The centerer can thus be shut off entirely by setting
the threshold to a large value >> 1 (meaning, no location inside the
search window will achieve that signal-to-noise ratio).
A note of caution: the ellipse fitting algorithm was designed
explicitly with an elliptical galaxy brightness distribution in
mind. In particular, a well defined negative radial intensity
gradient across the region being fitted is paramount for the
achievement of stable solutions. Use of the algorithm in other types
of images (e.g., planetary nebulae) may lead to inability to
converge to any acceptable solution.
"""
def __init__(self, image, geometry=None, threshold=0.1):
self.image = image
if geometry is not None:
self._geometry = geometry
else:
_x0 = image.shape[1] / 2
_y0 = image.shape[0] / 2
self._geometry = EllipseGeometry(_x0, _y0, 10.0, eps=0.2,
pa=np.pi / 2)
self.set_threshold(threshold)
[docs]
def set_threshold(self, threshold):
"""
Modify the threshold value used by the centerer.
Parameters
----------
threshold : float
The new threshold value to use.
"""
self._geometry.centerer_threshold = threshold
[docs]
def fit_image(self, sma0=None, minsma=0.0, maxsma=None, step=0.1,
conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT,
maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG,
maxgerr=DEFAULT_MAXGERR, sclip=3.0, nclip=0,
integrmode=BILINEAR, linear=None, maxrit=None,
fix_center=False, fix_pa=False, fix_eps=False):
# This parameter list is quite large and should in principle be
# simplified by re-distributing these controls to somewhere else.
# We keep this design though because it better mimics the flat
# architecture used in the original STSDAS task `ellipse`.
"""
Fit multiple isophotes to the image array.
This method loops over each value of the semimajor axis (sma)
length (constructed from the input parameters), fitting a single
isophote at each sma. The entire set of isophotes is returned
in an `~photutils.isophote.IsophoteList` instance.
Note that the fix_XXX parameters act in unison. Meaning, if one
of them is set via this call, the others will assume their default
(False) values. This effectively overrides any settings that are
present in the internal `~photutils.isophote.EllipseGeometry`
instance that is carried along as a property of this class. If
an instance of `~photutils.isophote.EllipseGeometry` was passed
to this class' constructor, that instance will be effectively
overridden by the fix_XXX parameters in this call.
Parameters
----------
sma0 : float, optional
The starting value for the semimajor axis length (pixels).
This value must not be the minimum or maximum semimajor axis
length, but something in between. The algorithm can't start
from the very center of the galaxy image because the
modelling of elliptical isophotes on that region is poor and
it will diverge very easily if not tied to other previously
fit isophotes. It can't start from the maximum value either
because the maximum is not known beforehand, depending on
signal-to-noise. The ``sma0`` value should be selected such
that the corresponding isophote has a good signal-to-noise
ratio and a clearly defined geometry. If set to `None` (the
default), one of two actions will be taken: if a
`~photutils.isophote.EllipseGeometry` instance was input to
the `~photutils.isophote.Ellipse` constructor, its ``sma``
value will be used. Otherwise, a default value of 10. will
be used.
minsma : float, optional
The minimum value for the semimajor axis length (pixels).
The default is 0.
maxsma : float or `None`, optional
The maximum value for the semimajor axis length (pixels).
When set to `None` (default), the algorithm will increase
the semimajor axis until one of several conditions will
cause it to stop and revert to fit ellipses with sma <
``sma0``.
step : float, optional
The step value used to grow/shrink the semimajor axis length
(pixels if ``linear=True``, or a relative value if
``linear=False``). See the ``linear`` parameter. The
default is 0.1.
conver : float, optional
The main convergence criterion. Iterations stop when the
largest harmonic amplitude becomes smaller (in absolute
value) than ``conver`` times the harmonic fit rms. The
default is 0.05.
minit : int, optional
The minimum number of iterations to perform. A minimum of 10
(the default) iterations guarantees that, on average, 2
iterations will be available for fitting each independent
parameter (the four harmonic amplitudes and the intensity
level). For the first isophote, the minimum number of
iterations is 2 * ``minit`` to ensure that, even departing
from not-so-good initial values, the algorithm has a better
chance to converge to a sensible solution.
maxit : int, optional
The maximum number of iterations to perform. The default is
50.
fflag : float, optional
The acceptable fraction of flagged data points in the
sample. If the actual fraction of valid data points is
smaller than this, the iterations will stop and the current
`~photutils.isophote.Isophote` will be returned. Flagged
data points are points that either lie outside the image
frame, are masked, or were rejected by sigma-clipping. The
default is 0.7.
maxgerr : float, optional
The maximum acceptable relative error in the local radial
intensity gradient. This is the main control for preventing
ellipses to grow to regions of too low signal-to-noise
ratio. It specifies the maximum acceptable relative error
in the local radial intensity gradient. `Busko (1996; ASPC
101, 139)
<https://ui.adsabs.harvard.edu/abs/1996ASPC..101..139B/abstract>`_
showed that the fitting precision relates to that relative
error. The usual behavior of the gradient relative error is
to increase with semimajor axis, being larger in outer,
fainter regions of a galaxy image. In the current
implementation, the ``maxgerr`` criterion is triggered only
when two consecutive isophotes exceed the value specified by
the parameter. This prevents premature stopping caused by
contamination such as stars and HII regions.
A number of actions may happen when the gradient error
exceeds ``maxgerr`` (or becomes non-significant and is set
to `None`). If the maximum semimajor axis specified by
``maxsma`` is set to `None`, semimajor axis growth is
stopped and the algorithm proceeds inwards to the galaxy
center. If ``maxsma`` is set to some finite value, and this
value is larger than the current semimajor axis length, the
algorithm enters non-iterative mode and proceeds outwards
until reaching ``maxsma``. The default is 0.5.
sclip : float, optional
The sigma-clip sigma value. The default is 3.0.
nclip : int, optional
The number of sigma-clip iterations. The default is 0, which
means sigma-clipping is skipped.
integrmode : {'bilinear', 'nearest_neighbor', 'mean', 'median'}, optional
The area integration mode. The default is 'bilinear'.
linear : bool, optional
The semimajor axis growing/shrinking mode. If `False`
(default), the geometric growing mode is chosen, thus the
semimajor axis length is increased by a factor of (1. +
``step``), and the process is repeated until either the
semimajor axis value reaches the value of parameter
``maxsma``, or the last fitted ellipse has more than a given
fraction of its sampled points flagged out (see ``fflag``).
The process then resumes from the first fitted ellipse (at
``sma0``) inwards, in steps of (1./(1. + ``step``)), until
the semimajor axis length reaches the value ``minsma``. In
case of linear growing, the increment or decrement value is
given directly by ``step`` in pixels. If ``maxsma`` is set
to `None`, the semimajor axis will grow until a low
signal-to-noise criterion is met. See ``maxgerr``.
maxrit : float or `None`, optional
The maximum value of semimajor axis to perform an actual
fit. Whenever the current semimajor axis length is larger
than ``maxrit``, the isophotes will be extracted using the
current geometry, without being fitted. This non-iterative
mode may be useful for sampling regions of very low surface
brightness, where the algorithm may become unstable and
unable to recover reliable geometry information.
Non-iterative mode can also be entered automatically
whenever the ellipticity exceeds 1.0 or the ellipse center
crosses the image boundaries. If `None` (default), then no
maximum value is used.
fix_center : bool, optional
Keep center of ellipse fixed during fit? The default is False.
fix_pa : bool, optional
Keep position angle of semi-major axis of ellipse fixed during fit?
The default is False.
fix_eps : bool, optional
Keep ellipticity of ellipse fixed during fit? The default is False.
Returns
-------
result : `~photutils.isophote.IsophoteList` instance
A list-like object of `~photutils.isophote.Isophote`
instances, sorted by increasing semimajor axis length.
"""
# multiple fitted isophotes will be stored here
isophote_list = []
# get starting sma from appropriate source: keyword parameter,
# internal EllipseGeometry instance, or fixed default value.
if not sma0:
if self._geometry:
sma = self._geometry.sma
else:
sma = 10.0
else:
sma = sma0
# Override geometry instance with parameters set at the call.
if isinstance(linear, bool):
self._geometry.linear_growth = linear
else:
linear = self._geometry.linear_growth
if fix_center and fix_pa and fix_eps:
warnings.warn(': Everything is fixed. Fit not possible.',
AstropyUserWarning)
return IsophoteList([])
if fix_center or fix_pa or fix_eps:
# Note that this overrides the geometry instance for good.
self._geometry.fix = np.array([fix_center, fix_center, fix_pa,
fix_eps])
# first, go from initial sma outwards until
# hitting one of several stopping criteria.
noiter = False
first_isophote = True
while True:
# first isophote runs longer
minit_a = 2 * minit if first_isophote else minit
first_isophote = False
isophote = self.fit_isophote(sma, step, conver, minit_a, maxit,
fflag, maxgerr, sclip, nclip,
integrmode, linear, maxrit,
noniterate=noiter,
isophote_list=isophote_list)
# check for failed fit.
if isophote.stop_code < 0 or isophote.stop_code == 1:
# in case the fit failed right at the outset, return an
# empty list. This is the usual case when the user
# provides initial guesses that are too way off to enable
# the fitting algorithm to find any meaningful solution.
if len(isophote_list) == 1:
warnings.warn('No meaningful fit was possible.',
AstropyUserWarning)
return IsophoteList([])
self._fix_last_isophote(isophote_list, -1)
# get last isophote from the actual list, since the last
# `isophote` instance in this context may no longer be OK.
isophote = isophote_list[-1]
# if two consecutive isophotes failed to fit,
# shut off iterative mode. Or, bail out and
# change to go inwards.
if len(isophote_list) > 2:
if ((isophote.stop_code == 5
and isophote_list[-2].stop_code == 5)
or isophote.stop_code == 1):
if maxsma and maxsma > isophote.sma:
# if a maximum sma value was provided by
# user, and the current sma is smaller than
# maxsma, keep growing sma in non-iterative
# mode until reaching it.
noiter = True
else:
# if no maximum sma, stop growing and change
# to go inwards.
break
# reset variable from the actual list, since the last
# `isophote` instance may no longer be OK.
isophote = isophote_list[-1]
# update sma. If exceeded user-defined
# maximum, bail out from this loop.
sma = isophote.sample.geometry.update_sma(step)
if maxsma and sma >= maxsma:
break
# reset sma so as to go inwards.
first_isophote = isophote_list[0]
sma, step = first_isophote.sample.geometry.reset_sma(step)
# now, go from initial sma inwards towards center.
while True:
isophote = self.fit_isophote(sma, step, conver, minit, maxit,
fflag, maxgerr, sclip, nclip,
integrmode, linear, maxrit,
going_inwards=True,
isophote_list=isophote_list)
# if abnormal condition, fix isophote but keep going.
if isophote.stop_code < 0:
self._fix_last_isophote(isophote_list, 0)
# but if we get an error from the scipy fitter, bail out
# immediately. This usually happens at very small radii
# when the number of data points is too small.
if isophote.stop_code == 3:
break
# reset variable from the actual list, since the last
# `isophote` instance may no longer be OK.
isophote = isophote_list[-1]
# figure out next sma; if exceeded user-defined
# minimum, or too small, bail out from this loop
sma = isophote.sample.geometry.update_sma(step)
if sma <= max(minsma, 0.5):
break
# if user asked for minsma=0, extract special isophote there
if minsma == 0.0:
# isophote is appended to isophote_list
_ = self.fit_isophote(0.0, isophote_list=isophote_list)
# sort list of isophotes according to sma
isophote_list.sort()
return IsophoteList(isophote_list)
[docs]
def fit_isophote(self, sma, step=0.1, conver=DEFAULT_CONVERGENCE,
minit=DEFAULT_MINIT, maxit=DEFAULT_MAXIT,
fflag=DEFAULT_FFLAG, maxgerr=DEFAULT_MAXGERR,
sclip=3.0, nclip=0, integrmode=BILINEAR,
linear=False, maxrit=None, noniterate=False,
going_inwards=False, isophote_list=None):
"""
Fit a single isophote with a given semimajor axis length.
The ``step`` and ``linear`` parameters are not used to actually
grow or shrink the current fitting semimajor axis length. They
are necessary so the sampling algorithm can know where to start
the gradient computation and also how to compute the elliptical
sector areas (when area integration mode is selected).
Parameters
----------
sma : float
The semimajor axis length (pixels).
step : float, optional
The step value used to grow/shrink the semimajor axis length
(pixels if ``linear=True``, or a relative value if
``linear=False``). See the ``linear`` parameter. The
default is 0.1.
conver : float, optional
The main convergence criterion. Iterations stop when the
largest harmonic amplitude becomes smaller (in absolute
value) than ``conver`` times the harmonic fit rms. The
default is 0.05.
minit : int, optional
The minimum number of iterations to perform. A minimum of 10
(the default) iterations guarantees that, on average, 2
iterations will be available for fitting each independent
parameter (the four harmonic amplitudes and the intensity
level). For the first isophote, the minimum number of
iterations is 2 * ``minit`` to ensure that, even departing
from not-so-good initial values, the algorithm has a better
chance to converge to a sensible solution.
maxit : int, optional
The maximum number of iterations to perform. The default is
50.
fflag : float, optional
The acceptable fraction of flagged data points in the
sample. If the actual fraction of valid data points is
smaller than this, the iterations will stop and the current
`~photutils.isophote.Isophote` will be returned. Flagged
data points are points that either lie outside the image
frame, are masked, or were rejected by sigma-clipping. The
default is 0.7.
maxgerr : float, optional
The maximum acceptable relative error in the local radial
intensity gradient. When fitting a single isophote by itself
this parameter doesn't have any effect on the outcome.
sclip : float, optional
The sigma-clip sigma value. The default is 3.0.
nclip : int, optional
The number of sigma-clip iterations. The default is 0, which
means sigma-clipping is skipped.
integrmode : {'bilinear', 'nearest_neighbor', 'mean', 'median'}, optional
The area integration mode. The default is 'bilinear'.
linear : bool, optional
The semimajor axis growing/shrinking mode. When fitting
just one isophote, this parameter is used only by the code
that define the details of how elliptical arc segments
("sectors") are extracted from the image when using area
extraction modes (see the ``integrmode`` parameter).
maxrit : float or `None`, optional
The maximum value of semimajor axis to perform an actual
fit. Whenever the current semimajor axis length is larger
than ``maxrit``, the isophotes will be extracted using the
current geometry, without being fitted. This non-iterative
mode may be useful for sampling regions of very low surface
brightness, where the algorithm may become unstable and
unable to recover reliable geometry information.
Non-iterative mode can also be entered automatically
whenever the ellipticity exceeds 1.0 or the ellipse center
crosses the image boundaries. If `None` (default), then no
maximum value is used.
noniterate : bool, optional
Whether the fitting algorithm should be bypassed and an
isophote should be extracted with the geometry taken
directly from the most recent `~photutils.isophote.Isophote`
instance stored in the ``isophote_list`` parameter. This
parameter is mainly used when running the method in a loop
over different values of semimajor axis length, and we want
to change from iterative to non-iterative mode somewhere
along the sequence of isophotes. When set to `True`, this
parameter overrides the behavior associated with parameter
``maxrit``. The default is `False`.
going_inwards : bool, optional
Parameter to define the sense of SMA growth. When fitting
just one isophote, this parameter is used only by the code
that defines the details of how elliptical arc segments
("sectors") are extracted from the image, when using area
extraction modes (see the ``integrmode`` parameter). The
default is `False`.
isophote_list : list or `None`, optional
If not `None` (the default), the fitted
`~photutils.isophote.Isophote` instance is appended to this
list. It must be created and managed by the caller.
Returns
-------
result : `~photutils.isophote.Isophote` instance
The fitted isophote. The fitted isophote is also appended to
the input list input to the ``isophote_list`` parameter.
"""
geometry = self._geometry
# if available, geometry from last fitted isophote will be
# used as initial guess for next isophote.
if isophote_list:
geometry = isophote_list[-1].sample.geometry
# do the fit
if noniterate or (maxrit and sma > maxrit):
isophote = self._non_iterative(sma, step, linear, geometry,
sclip, nclip, integrmode)
else:
isophote = self._iterative(sma, step, linear, geometry, sclip,
nclip, integrmode, conver, minit,
maxit, fflag, maxgerr, going_inwards)
# store result in list
if isophote_list is not None and isophote.valid:
isophote_list.append(isophote)
return isophote
def _iterative(self, sma, step, linear, geometry, sclip, nclip,
integrmode, conver, minit, maxit, fflag, maxgerr,
going_inwards=False):
if sma > 0.0:
# iterative fitter
sample = EllipseSample(self.image, sma, astep=step, sclip=sclip,
nclip=nclip, linear_growth=linear,
geometry=geometry, integrmode=integrmode)
fitter = EllipseFitter(sample)
else:
# sma == 0 requires special handling
sample = CentralEllipseSample(self.image, 0.0, geometry=geometry)
fitter = CentralEllipseFitter(sample)
isophote = fitter.fit(conver, minit, maxit, fflag, maxgerr,
going_inwards)
return isophote
def _non_iterative(self, sma, step, linear, geometry, sclip, nclip,
integrmode):
sample = EllipseSample(self.image, sma, astep=step, sclip=sclip,
nclip=nclip, linear_growth=linear,
geometry=geometry, integrmode=integrmode)
sample.update(geometry.fix)
# build isophote without iterating with an EllipseFitter
isophote = Isophote(sample, 0, True, stop_code=4)
return isophote
@staticmethod
def _fix_last_isophote(isophote_list, index):
if isophote_list:
isophote = isophote_list.pop()
# check if isophote is bad; if so, fix its geometry
# to be like the geometry of the index-th isophote
# in list.
isophote.fix_geometry(isophote_list[index])
# force new extraction of raw data, since
# geometry changed.
isophote.sample.values = None
isophote.sample.update(isophote.sample.geometry.fix)
# we take the opportunity to change an eventual
# negative stop code to its' positive equivalent.
code = 5 if isophote.stop_code < 0 else isophote.stop_code
# build new instance so it can have its attributes
# populated from the updated sample attributes.
new_isophote = Isophote(isophote.sample, isophote.niter,
isophote.valid, code)
# add new isophote to list
isophote_list.append(new_isophote)