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astropy/photutils | photutils/utils/convolution.py | filter_data | def filter_data(data, kernel, mode='constant', fill_value=0.0,
check_normalization=False):
"""
Convolve a 2D image with a 2D kernel.
The kernel may either be a 2D `~numpy.ndarray` or a
`~astropy.convolution.Kernel2D` object.
Parameters
----------
data : array_like
The 2D array of the image.
kernel : array-like (2D) or `~astropy.convolution.Kernel2D`
The 2D kernel used to filter the input ``data``. Filtering the
``data`` will smooth the noise and maximize detectability of
objects with a shape similar to the kernel.
mode : {'constant', 'reflect', 'nearest', 'mirror', 'wrap'}, optional
The ``mode`` determines how the array borders are handled. For
the ``'constant'`` mode, values outside the array borders are
set to ``fill_value``. The default is ``'constant'``.
fill_value : scalar, optional
Value to fill data values beyond the array borders if ``mode``
is ``'constant'``. The default is ``0.0``.
check_normalization : bool, optional
If `True` then a warning will be issued if the kernel is not
normalized to 1.
"""
from scipy import ndimage
if kernel is not None:
if isinstance(kernel, Kernel2D):
kernel_array = kernel.array
else:
kernel_array = kernel
if check_normalization:
if not np.allclose(np.sum(kernel_array), 1.0):
warnings.warn('The kernel is not normalized.',
AstropyUserWarning)
# NOTE: astropy.convolution.convolve fails with zero-sum
# kernels (used in findstars) (cf. astropy #1647)
# NOTE: if data is int and kernel is float, ndimage.convolve
# will return an int image - here we make the data float so
# that a float image is always returned
return ndimage.convolve(data.astype(float), kernel_array, mode=mode,
cval=fill_value)
else:
return data | python | def filter_data(data, kernel, mode='constant', fill_value=0.0,
check_normalization=False):
"""
Convolve a 2D image with a 2D kernel.
The kernel may either be a 2D `~numpy.ndarray` or a
`~astropy.convolution.Kernel2D` object.
Parameters
----------
data : array_like
The 2D array of the image.
kernel : array-like (2D) or `~astropy.convolution.Kernel2D`
The 2D kernel used to filter the input ``data``. Filtering the
``data`` will smooth the noise and maximize detectability of
objects with a shape similar to the kernel.
mode : {'constant', 'reflect', 'nearest', 'mirror', 'wrap'}, optional
The ``mode`` determines how the array borders are handled. For
the ``'constant'`` mode, values outside the array borders are
set to ``fill_value``. The default is ``'constant'``.
fill_value : scalar, optional
Value to fill data values beyond the array borders if ``mode``
is ``'constant'``. The default is ``0.0``.
check_normalization : bool, optional
If `True` then a warning will be issued if the kernel is not
normalized to 1.
"""
from scipy import ndimage
if kernel is not None:
if isinstance(kernel, Kernel2D):
kernel_array = kernel.array
else:
kernel_array = kernel
if check_normalization:
if not np.allclose(np.sum(kernel_array), 1.0):
warnings.warn('The kernel is not normalized.',
AstropyUserWarning)
# NOTE: astropy.convolution.convolve fails with zero-sum
# kernels (used in findstars) (cf. astropy #1647)
# NOTE: if data is int and kernel is float, ndimage.convolve
# will return an int image - here we make the data float so
# that a float image is always returned
return ndimage.convolve(data.astype(float), kernel_array, mode=mode,
cval=fill_value)
else:
return data | [
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The kernel may either be a 2D `~numpy.ndarray` or a
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Parameters
----------
data : array_like
The 2D array of the image.
kernel : array-like (2D) or `~astropy.convolution.Kernel2D`
The 2D kernel used to filter the input ``data``. Filtering the
``data`` will smooth the noise and maximize detectability of
objects with a shape similar to the kernel.
mode : {'constant', 'reflect', 'nearest', 'mirror', 'wrap'}, optional
The ``mode`` determines how the array borders are handled. For
the ``'constant'`` mode, values outside the array borders are
set to ``fill_value``. The default is ``'constant'``.
fill_value : scalar, optional
Value to fill data values beyond the array borders if ``mode``
is ``'constant'``. The default is ``0.0``.
check_normalization : bool, optional
If `True` then a warning will be issued if the kernel is not
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astropy/photutils | photutils/psf/utils.py | prepare_psf_model | def prepare_psf_model(psfmodel, xname=None, yname=None, fluxname=None,
renormalize_psf=True):
"""
Convert a 2D PSF model to one suitable for use with
`BasicPSFPhotometry` or its subclasses.
The resulting model may be a composite model, but should have only
the x, y, and flux related parameters un-fixed.
Parameters
----------
psfmodel : a 2D model
The model to assume as representative of the PSF.
xname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
x-axis center of the PSF. If None, the model will be assumed to
be centered at x=0, and a new parameter will be added for the
offset.
yname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
y-axis center of the PSF. If None, the model will be assumed to
be centered at y=0, and a new parameter will be added for the
offset.
fluxname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
total flux of the star. If None, a scaling factor will be added
to the model.
renormalize_psf : bool
If True, the model will be integrated from -inf to inf and
re-scaled so that the total integrates to 1. Note that this
renormalization only occurs *once*, so if the total flux of
``psfmodel`` depends on position, this will *not* be correct.
Returns
-------
outmod : a model
A new model ready to be passed into `BasicPSFPhotometry` or its
subclasses.
"""
if xname is None:
xinmod = models.Shift(0, name='x_offset')
xname = 'offset_0'
else:
xinmod = models.Identity(1)
xname = xname + '_2'
xinmod.fittable = True
if yname is None:
yinmod = models.Shift(0, name='y_offset')
yname = 'offset_1'
else:
yinmod = models.Identity(1)
yname = yname + '_2'
yinmod.fittable = True
outmod = (xinmod & yinmod) | psfmodel
if fluxname is None:
outmod = outmod * models.Const2D(1, name='flux_scaling')
fluxname = 'amplitude_3'
else:
fluxname = fluxname + '_2'
if renormalize_psf:
# we do the import here because other machinery works w/o scipy
from scipy import integrate
integrand = integrate.dblquad(psfmodel, -np.inf, np.inf,
lambda x: -np.inf, lambda x: np.inf)[0]
normmod = models.Const2D(1./integrand, name='renormalize_scaling')
outmod = outmod * normmod
# final setup of the output model - fix all the non-offset/scale
# parameters
for pnm in outmod.param_names:
outmod.fixed[pnm] = pnm not in (xname, yname, fluxname)
# and set the names so that BasicPSFPhotometry knows what to do
outmod.xname = xname
outmod.yname = yname
outmod.fluxname = fluxname
# now some convenience aliases if reasonable
outmod.psfmodel = outmod[2]
if 'x_0' not in outmod.param_names and 'y_0' not in outmod.param_names:
outmod.x_0 = getattr(outmod, xname)
outmod.y_0 = getattr(outmod, yname)
if 'flux' not in outmod.param_names:
outmod.flux = getattr(outmod, fluxname)
return outmod | python | def prepare_psf_model(psfmodel, xname=None, yname=None, fluxname=None,
renormalize_psf=True):
"""
Convert a 2D PSF model to one suitable for use with
`BasicPSFPhotometry` or its subclasses.
The resulting model may be a composite model, but should have only
the x, y, and flux related parameters un-fixed.
Parameters
----------
psfmodel : a 2D model
The model to assume as representative of the PSF.
xname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
x-axis center of the PSF. If None, the model will be assumed to
be centered at x=0, and a new parameter will be added for the
offset.
yname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
y-axis center of the PSF. If None, the model will be assumed to
be centered at y=0, and a new parameter will be added for the
offset.
fluxname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
total flux of the star. If None, a scaling factor will be added
to the model.
renormalize_psf : bool
If True, the model will be integrated from -inf to inf and
re-scaled so that the total integrates to 1. Note that this
renormalization only occurs *once*, so if the total flux of
``psfmodel`` depends on position, this will *not* be correct.
Returns
-------
outmod : a model
A new model ready to be passed into `BasicPSFPhotometry` or its
subclasses.
"""
if xname is None:
xinmod = models.Shift(0, name='x_offset')
xname = 'offset_0'
else:
xinmod = models.Identity(1)
xname = xname + '_2'
xinmod.fittable = True
if yname is None:
yinmod = models.Shift(0, name='y_offset')
yname = 'offset_1'
else:
yinmod = models.Identity(1)
yname = yname + '_2'
yinmod.fittable = True
outmod = (xinmod & yinmod) | psfmodel
if fluxname is None:
outmod = outmod * models.Const2D(1, name='flux_scaling')
fluxname = 'amplitude_3'
else:
fluxname = fluxname + '_2'
if renormalize_psf:
# we do the import here because other machinery works w/o scipy
from scipy import integrate
integrand = integrate.dblquad(psfmodel, -np.inf, np.inf,
lambda x: -np.inf, lambda x: np.inf)[0]
normmod = models.Const2D(1./integrand, name='renormalize_scaling')
outmod = outmod * normmod
# final setup of the output model - fix all the non-offset/scale
# parameters
for pnm in outmod.param_names:
outmod.fixed[pnm] = pnm not in (xname, yname, fluxname)
# and set the names so that BasicPSFPhotometry knows what to do
outmod.xname = xname
outmod.yname = yname
outmod.fluxname = fluxname
# now some convenience aliases if reasonable
outmod.psfmodel = outmod[2]
if 'x_0' not in outmod.param_names and 'y_0' not in outmod.param_names:
outmod.x_0 = getattr(outmod, xname)
outmod.y_0 = getattr(outmod, yname)
if 'flux' not in outmod.param_names:
outmod.flux = getattr(outmod, fluxname)
return outmod | [
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Parameters
----------
psfmodel : a 2D model
The model to assume as representative of the PSF.
xname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
x-axis center of the PSF. If None, the model will be assumed to
be centered at x=0, and a new parameter will be added for the
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yname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
y-axis center of the PSF. If None, the model will be assumed to
be centered at y=0, and a new parameter will be added for the
offset.
fluxname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
total flux of the star. If None, a scaling factor will be added
to the model.
renormalize_psf : bool
If True, the model will be integrated from -inf to inf and
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Returns
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outmod : a model
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astropy/photutils | photutils/psf/utils.py | get_grouped_psf_model | def get_grouped_psf_model(template_psf_model, star_group, pars_to_set):
"""
Construct a joint PSF model which consists of a sum of PSF's templated on
a specific model, but whose parameters are given by a table of objects.
Parameters
----------
template_psf_model : `astropy.modeling.Fittable2DModel` instance
The model to use for *individual* objects. Must have parameters named
``x_0``, ``y_0``, and ``flux``.
star_group : `~astropy.table.Table`
Table of stars for which the compound PSF will be constructed. It
must have columns named ``x_0``, ``y_0``, and ``flux_0``.
Returns
-------
group_psf
An `astropy.modeling` ``CompoundModel`` instance which is a sum of the
given PSF models.
"""
group_psf = None
for star in star_group:
psf_to_add = template_psf_model.copy()
for param_tab_name, param_name in pars_to_set.items():
setattr(psf_to_add, param_name, star[param_tab_name])
if group_psf is None:
# this is the first one only
group_psf = psf_to_add
else:
group_psf += psf_to_add
return group_psf | python | def get_grouped_psf_model(template_psf_model, star_group, pars_to_set):
"""
Construct a joint PSF model which consists of a sum of PSF's templated on
a specific model, but whose parameters are given by a table of objects.
Parameters
----------
template_psf_model : `astropy.modeling.Fittable2DModel` instance
The model to use for *individual* objects. Must have parameters named
``x_0``, ``y_0``, and ``flux``.
star_group : `~astropy.table.Table`
Table of stars for which the compound PSF will be constructed. It
must have columns named ``x_0``, ``y_0``, and ``flux_0``.
Returns
-------
group_psf
An `astropy.modeling` ``CompoundModel`` instance which is a sum of the
given PSF models.
"""
group_psf = None
for star in star_group:
psf_to_add = template_psf_model.copy()
for param_tab_name, param_name in pars_to_set.items():
setattr(psf_to_add, param_name, star[param_tab_name])
if group_psf is None:
# this is the first one only
group_psf = psf_to_add
else:
group_psf += psf_to_add
return group_psf | [
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astropy/photutils | photutils/psf/utils.py | _call_fitter | def _call_fitter(fitter, psf, x, y, data, weights):
"""
Not all fitters have to support a weight array. This function
includes the weight in the fitter call only if really needed.
"""
if np.all(weights == 1.):
return fitter(psf, x, y, data)
else:
return fitter(psf, x, y, data, weights=weights) | python | def _call_fitter(fitter, psf, x, y, data, weights):
"""
Not all fitters have to support a weight array. This function
includes the weight in the fitter call only if really needed.
"""
if np.all(weights == 1.):
return fitter(psf, x, y, data)
else:
return fitter(psf, x, y, data, weights=weights) | [
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astropy/photutils | photutils/detection/core.py | detect_threshold | def detect_threshold(data, snr, background=None, error=None, mask=None,
mask_value=None, sigclip_sigma=3.0, sigclip_iters=None):
"""
Calculate a pixel-wise threshold image that can be used to detect
sources.
Parameters
----------
data : array_like
The 2D array of the image.
snr : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
background : float or array_like, optional
The background value(s) of the input ``data``. ``background``
may either be a scalar value or a 2D image with the same shape
as the input ``data``. If the input ``data`` has been
background-subtracted, then set ``background`` to ``0.0``. If
`None`, then a scalar background value will be estimated using
sigma-clipped statistics.
error : float or array_like, optional
The Gaussian 1-sigma standard deviation of the background noise
in ``data``. ``error`` should include all sources of
"background" error, but *exclude* the Poisson error of the
sources. If ``error`` is a 2D image, then it should represent
the 1-sigma background error in each pixel of ``data``. If
`None`, then a scalar background rms value will be estimated
using sigma-clipped statistics.
mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
when calculating the image background statistics.
sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
clip until convergence is achieved (i.e., continue until the last
iteration clips nothing) when calculating the image background
statistics.
Returns
-------
threshold : 2D `~numpy.ndarray`
A 2D image with the same shape as ``data`` containing the
pixel-wise threshold values.
See Also
--------
:func:`photutils.segmentation.detect_sources`
Notes
-----
The ``mask``, ``mask_value``, ``sigclip_sigma``, and
``sigclip_iters`` inputs are used only if it is necessary to
estimate ``background`` or ``error`` using sigma-clipped background
statistics. If ``background`` and ``error`` are both input, then
``mask``, ``mask_value``, ``sigclip_sigma``, and ``sigclip_iters``
are ignored.
"""
if background is None or error is None:
if astropy_version < '3.1':
data_mean, data_median, data_std = sigma_clipped_stats(
data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
iters=sigclip_iters)
else:
data_mean, data_median, data_std = sigma_clipped_stats(
data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
maxiters=sigclip_iters)
bkgrd_image = np.zeros_like(data) + data_mean
bkgrdrms_image = np.zeros_like(data) + data_std
if background is None:
background = bkgrd_image
else:
if np.isscalar(background):
background = np.zeros_like(data) + background
else:
if background.shape != data.shape:
raise ValueError('If input background is 2D, then it '
'must have the same shape as the input '
'data.')
if error is None:
error = bkgrdrms_image
else:
if np.isscalar(error):
error = np.zeros_like(data) + error
else:
if error.shape != data.shape:
raise ValueError('If input error is 2D, then it '
'must have the same shape as the input '
'data.')
return background + (error * snr) | python | def detect_threshold(data, snr, background=None, error=None, mask=None,
mask_value=None, sigclip_sigma=3.0, sigclip_iters=None):
"""
Calculate a pixel-wise threshold image that can be used to detect
sources.
Parameters
----------
data : array_like
The 2D array of the image.
snr : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
background : float or array_like, optional
The background value(s) of the input ``data``. ``background``
may either be a scalar value or a 2D image with the same shape
as the input ``data``. If the input ``data`` has been
background-subtracted, then set ``background`` to ``0.0``. If
`None`, then a scalar background value will be estimated using
sigma-clipped statistics.
error : float or array_like, optional
The Gaussian 1-sigma standard deviation of the background noise
in ``data``. ``error`` should include all sources of
"background" error, but *exclude* the Poisson error of the
sources. If ``error`` is a 2D image, then it should represent
the 1-sigma background error in each pixel of ``data``. If
`None`, then a scalar background rms value will be estimated
using sigma-clipped statistics.
mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
when calculating the image background statistics.
sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
clip until convergence is achieved (i.e., continue until the last
iteration clips nothing) when calculating the image background
statistics.
Returns
-------
threshold : 2D `~numpy.ndarray`
A 2D image with the same shape as ``data`` containing the
pixel-wise threshold values.
See Also
--------
:func:`photutils.segmentation.detect_sources`
Notes
-----
The ``mask``, ``mask_value``, ``sigclip_sigma``, and
``sigclip_iters`` inputs are used only if it is necessary to
estimate ``background`` or ``error`` using sigma-clipped background
statistics. If ``background`` and ``error`` are both input, then
``mask``, ``mask_value``, ``sigclip_sigma``, and ``sigclip_iters``
are ignored.
"""
if background is None or error is None:
if astropy_version < '3.1':
data_mean, data_median, data_std = sigma_clipped_stats(
data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
iters=sigclip_iters)
else:
data_mean, data_median, data_std = sigma_clipped_stats(
data, mask=mask, mask_value=mask_value, sigma=sigclip_sigma,
maxiters=sigclip_iters)
bkgrd_image = np.zeros_like(data) + data_mean
bkgrdrms_image = np.zeros_like(data) + data_std
if background is None:
background = bkgrd_image
else:
if np.isscalar(background):
background = np.zeros_like(data) + background
else:
if background.shape != data.shape:
raise ValueError('If input background is 2D, then it '
'must have the same shape as the input '
'data.')
if error is None:
error = bkgrdrms_image
else:
if np.isscalar(error):
error = np.zeros_like(data) + error
else:
if error.shape != data.shape:
raise ValueError('If input error is 2D, then it '
'must have the same shape as the input '
'data.')
return background + (error * snr) | [
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The 2D array of the image.
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The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
background : float or array_like, optional
The background value(s) of the input ``data``. ``background``
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background-subtracted, then set ``background`` to ``0.0``. If
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error : float or array_like, optional
The Gaussian 1-sigma standard deviation of the background noise
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the 1-sigma background error in each pixel of ``data``. If
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mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
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sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
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Returns
-------
threshold : 2D `~numpy.ndarray`
A 2D image with the same shape as ``data`` containing the
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See Also
--------
:func:`photutils.segmentation.detect_sources`
Notes
-----
The ``mask``, ``mask_value``, ``sigclip_sigma``, and
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astropy/photutils | ah_bootstrap.py | run_cmd | def run_cmd(cmd):
"""
Run a command in a subprocess, given as a list of command-line
arguments.
Returns a ``(returncode, stdout, stderr)`` tuple.
"""
try:
p = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE)
# XXX: May block if either stdout or stderr fill their buffers;
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# unlikely (they should have very brief output)
stdout, stderr = p.communicate()
except OSError as e:
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raise
if e.errno == errno.ENOENT:
msg = 'Command not found: `{0}`'.format(' '.join(cmd))
raise _CommandNotFound(msg, cmd)
else:
raise _AHBootstrapSystemExit(
'An unexpected error occurred when running the '
'`{0}` command:\n{1}'.format(' '.join(cmd), str(e)))
# Can fail of the default locale is not configured properly. See
# https://github.com/astropy/astropy/issues/2749. For the purposes under
# consideration 'latin1' is an acceptable fallback.
try:
stdio_encoding = locale.getdefaultlocale()[1] or 'latin1'
except ValueError:
# Due to an OSX oddity locale.getdefaultlocale() can also crash
# depending on the user's locale/language settings. See:
# http://bugs.python.org/issue18378
stdio_encoding = 'latin1'
# Unlikely to fail at this point but even then let's be flexible
if not isinstance(stdout, str):
stdout = stdout.decode(stdio_encoding, 'replace')
if not isinstance(stderr, str):
stderr = stderr.decode(stdio_encoding, 'replace')
return (p.returncode, stdout, stderr) | python | def run_cmd(cmd):
"""
Run a command in a subprocess, given as a list of command-line
arguments.
Returns a ``(returncode, stdout, stderr)`` tuple.
"""
try:
p = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE)
# XXX: May block if either stdout or stderr fill their buffers;
# however for the commands this is currently used for that is
# unlikely (they should have very brief output)
stdout, stderr = p.communicate()
except OSError as e:
if DEBUG:
raise
if e.errno == errno.ENOENT:
msg = 'Command not found: `{0}`'.format(' '.join(cmd))
raise _CommandNotFound(msg, cmd)
else:
raise _AHBootstrapSystemExit(
'An unexpected error occurred when running the '
'`{0}` command:\n{1}'.format(' '.join(cmd), str(e)))
# Can fail of the default locale is not configured properly. See
# https://github.com/astropy/astropy/issues/2749. For the purposes under
# consideration 'latin1' is an acceptable fallback.
try:
stdio_encoding = locale.getdefaultlocale()[1] or 'latin1'
except ValueError:
# Due to an OSX oddity locale.getdefaultlocale() can also crash
# depending on the user's locale/language settings. See:
# http://bugs.python.org/issue18378
stdio_encoding = 'latin1'
# Unlikely to fail at this point but even then let's be flexible
if not isinstance(stdout, str):
stdout = stdout.decode(stdio_encoding, 'replace')
if not isinstance(stderr, str):
stderr = stderr.decode(stdio_encoding, 'replace')
return (p.returncode, stdout, stderr) | [
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astropy/photutils | photutils/aperture/ellipse.py | EllipticalAperture.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyEllipticalAperture` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyEllipticalAperture` object
A `SkyEllipticalAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyEllipticalAperture(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyEllipticalAperture` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyEllipticalAperture` object
A `SkyEllipticalAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyEllipticalAperture(**sky_params) | [
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astropy/photutils | photutils/aperture/ellipse.py | EllipticalAnnulus.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyEllipticalAnnulus` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyEllipticalAnnulus` object
A `SkyEllipticalAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyEllipticalAnnulus(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyEllipticalAnnulus` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyEllipticalAnnulus` object
A `SkyEllipticalAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyEllipticalAnnulus(**sky_params) | [
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astropy/photutils | photutils/aperture/ellipse.py | SkyEllipticalAperture.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to an `EllipticalAperture` object defined
in pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `EllipticalAperture` object
An `EllipticalAperture` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return EllipticalAperture(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to an `EllipticalAperture` object defined
in pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `EllipticalAperture` object
An `EllipticalAperture` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return EllipticalAperture(**pixel_params) | [
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astropy/photutils | photutils/aperture/ellipse.py | SkyEllipticalAnnulus.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to an `EllipticalAnnulus` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `EllipticalAnnulus` object
An `EllipticalAnnulus` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return EllipticalAnnulus(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to an `EllipticalAnnulus` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `EllipticalAnnulus` object
An `EllipticalAnnulus` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return EllipticalAnnulus(**pixel_params) | [
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Whether to do the transformation including distortions
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astropy/photutils | photutils/isophote/geometry.py | _area | def _area(sma, eps, phi, r):
"""
Compute elliptical sector area.
"""
aux = r * math.cos(phi) / sma
signal = aux / abs(aux)
if abs(aux) >= 1.:
aux = signal
return abs(sma**2 * (1.-eps) / 2. * math.acos(aux)) | python | def _area(sma, eps, phi, r):
"""
Compute elliptical sector area.
"""
aux = r * math.cos(phi) / sma
signal = aux / abs(aux)
if abs(aux) >= 1.:
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.find_center | def find_center(self, image, threshold=0.1, verbose=True):
"""
Find the center of a galaxy.
If the algorithm is successful the (x, y) coordinates in this
`~photutils.isophote.EllipseGeometry` (i.e. the ``x0`` and
``y0`` attributes) instance will be modified.
The isophote fit algorithm requires an initial guess for the
galaxy center (x, y) coordinates and these coordinates must be
close to the actual galaxy center for the isophote fit to work.
This method provides can provide an initial guess for the galaxy
center coordinates. See the **Notes** section below for more
details.
Parameters
----------
image : 2D `~numpy.ndarray`
The image array. Masked arrays are not recognized here. This
assumes that centering should always be done on valid pixels.
threshold : float, optional
The centerer threshold. To turn off the centerer, set this
to a large value (i.e. >> 1). The default is 0.1.
verbose : bool, optional
Whether to print object centering information. The default is
`True`.
Notes
-----
The centerer function scans a 10x10 window centered on the (x,
y) coordinates in the `~photutils.isophote.EllipseGeometry`
instance passed to the constructor of the
`~photutils.isophote.Ellipse` class. If any of the
`~photutils.isophote.EllipseGeometry` (x, y) coordinates are
`None`, the center of the input image frame is used. If the
center acquisition is successful, 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. 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 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
(i.e. >> 1; meaning no location inside the search window will
achieve that signal-to-noise ratio).
"""
self._centerer_mask_half_size = len(IN_MASK) / 2
self.centerer_threshold = threshold
# number of pixels in each mask
sz = len(IN_MASK)
self._centerer_ones_in = np.ma.masked_array(np.ones(shape=(sz, sz)),
mask=IN_MASK)
self._centerer_ones_out = np.ma.masked_array(np.ones(shape=(sz, sz)),
mask=OUT_MASK)
self._centerer_in_mask_npix = np.sum(self._centerer_ones_in)
self._centerer_out_mask_npix = np.sum(self._centerer_ones_out)
# Check if center coordinates point to somewhere inside the frame.
# If not, set then to frame center.
shape = image.shape
_x0 = self.x0
_y0 = self.y0
if (_x0 is None or _x0 < 0 or _x0 >= shape[1] or _y0 is None or
_y0 < 0 or _y0 >= shape[0]):
_x0 = shape[1] / 2
_y0 = shape[0] / 2
max_fom = 0.
max_i = 0
max_j = 0
# scan all positions inside window
window_half_size = 5
for i in range(int(_x0 - window_half_size),
int(_x0 + window_half_size) + 1):
for j in range(int(_y0 - window_half_size),
int(_y0 + window_half_size) + 1):
# ensure that it stays inside image frame
i1 = int(max(0, i - self._centerer_mask_half_size))
j1 = int(max(0, j - self._centerer_mask_half_size))
i2 = int(min(shape[1] - 1, i + self._centerer_mask_half_size))
j2 = int(min(shape[0] - 1, j + self._centerer_mask_half_size))
window = image[j1:j2, i1:i2]
# averages in inner and outer regions.
inner = np.ma.masked_array(window, mask=IN_MASK)
outer = np.ma.masked_array(window, mask=OUT_MASK)
inner_avg = np.sum(inner) / self._centerer_in_mask_npix
outer_avg = np.sum(outer) / self._centerer_out_mask_npix
# standard deviation and figure of merit
inner_std = np.std(inner)
outer_std = np.std(outer)
stddev = np.sqrt(inner_std**2 + outer_std**2)
fom = (inner_avg - outer_avg) / stddev
if fom > max_fom:
max_fom = fom
max_i = i
max_j = j
# figure of merit > threshold: update geometry with new coordinates.
if max_fom > threshold:
self.x0 = float(max_i)
self.y0 = float(max_j)
if verbose:
log.info("Found center at x0 = {0:5.1f}, y0 = {1:5.1f}"
.format(self.x0, self.y0))
else:
if verbose:
log.info('Result is below the threshold -- keeping the '
'original coordinates.') | python | def find_center(self, image, threshold=0.1, verbose=True):
"""
Find the center of a galaxy.
If the algorithm is successful the (x, y) coordinates in this
`~photutils.isophote.EllipseGeometry` (i.e. the ``x0`` and
``y0`` attributes) instance will be modified.
The isophote fit algorithm requires an initial guess for the
galaxy center (x, y) coordinates and these coordinates must be
close to the actual galaxy center for the isophote fit to work.
This method provides can provide an initial guess for the galaxy
center coordinates. See the **Notes** section below for more
details.
Parameters
----------
image : 2D `~numpy.ndarray`
The image array. Masked arrays are not recognized here. This
assumes that centering should always be done on valid pixels.
threshold : float, optional
The centerer threshold. To turn off the centerer, set this
to a large value (i.e. >> 1). The default is 0.1.
verbose : bool, optional
Whether to print object centering information. The default is
`True`.
Notes
-----
The centerer function scans a 10x10 window centered on the (x,
y) coordinates in the `~photutils.isophote.EllipseGeometry`
instance passed to the constructor of the
`~photutils.isophote.Ellipse` class. If any of the
`~photutils.isophote.EllipseGeometry` (x, y) coordinates are
`None`, the center of the input image frame is used. If the
center acquisition is successful, 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. 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 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
(i.e. >> 1; meaning no location inside the search window will
achieve that signal-to-noise ratio).
"""
self._centerer_mask_half_size = len(IN_MASK) / 2
self.centerer_threshold = threshold
# number of pixels in each mask
sz = len(IN_MASK)
self._centerer_ones_in = np.ma.masked_array(np.ones(shape=(sz, sz)),
mask=IN_MASK)
self._centerer_ones_out = np.ma.masked_array(np.ones(shape=(sz, sz)),
mask=OUT_MASK)
self._centerer_in_mask_npix = np.sum(self._centerer_ones_in)
self._centerer_out_mask_npix = np.sum(self._centerer_ones_out)
# Check if center coordinates point to somewhere inside the frame.
# If not, set then to frame center.
shape = image.shape
_x0 = self.x0
_y0 = self.y0
if (_x0 is None or _x0 < 0 or _x0 >= shape[1] or _y0 is None or
_y0 < 0 or _y0 >= shape[0]):
_x0 = shape[1] / 2
_y0 = shape[0] / 2
max_fom = 0.
max_i = 0
max_j = 0
# scan all positions inside window
window_half_size = 5
for i in range(int(_x0 - window_half_size),
int(_x0 + window_half_size) + 1):
for j in range(int(_y0 - window_half_size),
int(_y0 + window_half_size) + 1):
# ensure that it stays inside image frame
i1 = int(max(0, i - self._centerer_mask_half_size))
j1 = int(max(0, j - self._centerer_mask_half_size))
i2 = int(min(shape[1] - 1, i + self._centerer_mask_half_size))
j2 = int(min(shape[0] - 1, j + self._centerer_mask_half_size))
window = image[j1:j2, i1:i2]
# averages in inner and outer regions.
inner = np.ma.masked_array(window, mask=IN_MASK)
outer = np.ma.masked_array(window, mask=OUT_MASK)
inner_avg = np.sum(inner) / self._centerer_in_mask_npix
outer_avg = np.sum(outer) / self._centerer_out_mask_npix
# standard deviation and figure of merit
inner_std = np.std(inner)
outer_std = np.std(outer)
stddev = np.sqrt(inner_std**2 + outer_std**2)
fom = (inner_avg - outer_avg) / stddev
if fom > max_fom:
max_fom = fom
max_i = i
max_j = j
# figure of merit > threshold: update geometry with new coordinates.
if max_fom > threshold:
self.x0 = float(max_i)
self.y0 = float(max_j)
if verbose:
log.info("Found center at x0 = {0:5.1f}, y0 = {1:5.1f}"
.format(self.x0, self.y0))
else:
if verbose:
log.info('Result is below the threshold -- keeping the '
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If the algorithm is successful the (x, y) coordinates in this
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``y0`` attributes) instance will be modified.
The isophote fit algorithm requires an initial guess for the
galaxy center (x, y) coordinates and these coordinates must be
close to the actual galaxy center for the isophote fit to work.
This method provides can provide an initial guess for the galaxy
center coordinates. See the **Notes** section below for more
details.
Parameters
----------
image : 2D `~numpy.ndarray`
The image array. Masked arrays are not recognized here. This
assumes that centering should always be done on valid pixels.
threshold : float, optional
The centerer threshold. To turn off the centerer, set this
to a large value (i.e. >> 1). The default is 0.1.
verbose : bool, optional
Whether to print object centering information. The default is
`True`.
Notes
-----
The centerer function scans a 10x10 window centered on the (x,
y) coordinates in the `~photutils.isophote.EllipseGeometry`
instance passed to the constructor of the
`~photutils.isophote.Ellipse` class. If any of the
`~photutils.isophote.EllipseGeometry` (x, y) coordinates are
`None`, the center of the input image frame is used. If the
center acquisition is successful, 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. 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 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
(i.e. >> 1; meaning no location inside the search window will
achieve that signal-to-noise ratio). | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/isophote/geometry.py#L133-L254 | train |
astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.radius | def radius(self, angle):
"""
Calculate the polar radius for a given polar angle.
Parameters
----------
angle : float
The polar angle (radians).
Returns
-------
radius : float
The polar radius (pixels).
"""
return (self.sma * (1. - self.eps) /
np.sqrt(((1. - self.eps) * np.cos(angle))**2 +
(np.sin(angle))**2)) | python | def radius(self, angle):
"""
Calculate the polar radius for a given polar angle.
Parameters
----------
angle : float
The polar angle (radians).
Returns
-------
radius : float
The polar radius (pixels).
"""
return (self.sma * (1. - self.eps) /
np.sqrt(((1. - self.eps) * np.cos(angle))**2 +
(np.sin(angle))**2)) | [
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radius : float
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.initialize_sector_geometry | def initialize_sector_geometry(self, phi):
"""
Initialize geometry attributes associated with an elliptical
sector at the given polar angle ``phi``.
This function computes:
* the four vertices that define the elliptical sector on the
pixel array.
* the sector area (saved in the ``sector_area`` attribute)
* the sector angular width (saved in ``sector_angular_width``
attribute)
Parameters
----------
phi : float
The polar angle (radians) where the sector is located.
Returns
-------
x, y : 1D `~numpy.ndarray`
The x and y coordinates of each vertex as 1D arrays.
"""
# These polar radii bound the region between the inner
# and outer ellipses that define the sector.
sma1, sma2 = self.bounding_ellipses()
eps_ = 1. - self.eps
# polar vector at one side of the elliptical sector
self._phi1 = phi - self.sector_angular_width / 2.
r1 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
+ (math.sin(self._phi1))**2))
r2 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
+ (math.sin(self._phi1))**2))
# polar vector at the other side of the elliptical sector
self._phi2 = phi + self.sector_angular_width / 2.
r3 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
+ (math.sin(self._phi2))**2))
r4 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
+ (math.sin(self._phi2))**2))
# sector area
sa1 = _area(sma1, self.eps, self._phi1, r1)
sa2 = _area(sma2, self.eps, self._phi1, r2)
sa3 = _area(sma2, self.eps, self._phi2, r3)
sa4 = _area(sma1, self.eps, self._phi2, r4)
self.sector_area = abs((sa3 - sa2) - (sa4 - sa1))
# angular width of sector. It is calculated such that the sectors
# come out with roughly constant area along the ellipse.
self.sector_angular_width = max(min((self._area_factor / (r3 - r4) /
r4), self._phi_max),
self._phi_min)
# compute the 4 vertices that define the elliptical sector.
vertex_x = np.zeros(shape=4, dtype=float)
vertex_y = np.zeros(shape=4, dtype=float)
# vertices are labelled in counterclockwise sequence
vertex_x[0:2] = np.array([r1, r2]) * math.cos(self._phi1 + self.pa)
vertex_x[2:4] = np.array([r4, r3]) * math.cos(self._phi2 + self.pa)
vertex_y[0:2] = np.array([r1, r2]) * math.sin(self._phi1 + self.pa)
vertex_y[2:4] = np.array([r4, r3]) * math.sin(self._phi2 + self.pa)
vertex_x += self.x0
vertex_y += self.y0
return vertex_x, vertex_y | python | def initialize_sector_geometry(self, phi):
"""
Initialize geometry attributes associated with an elliptical
sector at the given polar angle ``phi``.
This function computes:
* the four vertices that define the elliptical sector on the
pixel array.
* the sector area (saved in the ``sector_area`` attribute)
* the sector angular width (saved in ``sector_angular_width``
attribute)
Parameters
----------
phi : float
The polar angle (radians) where the sector is located.
Returns
-------
x, y : 1D `~numpy.ndarray`
The x and y coordinates of each vertex as 1D arrays.
"""
# These polar radii bound the region between the inner
# and outer ellipses that define the sector.
sma1, sma2 = self.bounding_ellipses()
eps_ = 1. - self.eps
# polar vector at one side of the elliptical sector
self._phi1 = phi - self.sector_angular_width / 2.
r1 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
+ (math.sin(self._phi1))**2))
r2 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
+ (math.sin(self._phi1))**2))
# polar vector at the other side of the elliptical sector
self._phi2 = phi + self.sector_angular_width / 2.
r3 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
+ (math.sin(self._phi2))**2))
r4 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
+ (math.sin(self._phi2))**2))
# sector area
sa1 = _area(sma1, self.eps, self._phi1, r1)
sa2 = _area(sma2, self.eps, self._phi1, r2)
sa3 = _area(sma2, self.eps, self._phi2, r3)
sa4 = _area(sma1, self.eps, self._phi2, r4)
self.sector_area = abs((sa3 - sa2) - (sa4 - sa1))
# angular width of sector. It is calculated such that the sectors
# come out with roughly constant area along the ellipse.
self.sector_angular_width = max(min((self._area_factor / (r3 - r4) /
r4), self._phi_max),
self._phi_min)
# compute the 4 vertices that define the elliptical sector.
vertex_x = np.zeros(shape=4, dtype=float)
vertex_y = np.zeros(shape=4, dtype=float)
# vertices are labelled in counterclockwise sequence
vertex_x[0:2] = np.array([r1, r2]) * math.cos(self._phi1 + self.pa)
vertex_x[2:4] = np.array([r4, r3]) * math.cos(self._phi2 + self.pa)
vertex_y[0:2] = np.array([r1, r2]) * math.sin(self._phi1 + self.pa)
vertex_y[2:4] = np.array([r4, r3]) * math.sin(self._phi2 + self.pa)
vertex_x += self.x0
vertex_y += self.y0
return vertex_x, vertex_y | [
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.bounding_ellipses | def bounding_ellipses(self):
"""
Compute the semimajor axis of the two ellipses that bound the
annulus where integrations take place.
Returns
-------
sma1, sma2 : float
The smaller and larger values of semimajor axis length that
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"""
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a2 = self.sma * (1. + self.astep / 2.)
return a1, a2 | python | def bounding_ellipses(self):
"""
Compute the semimajor axis of the two ellipses that bound the
annulus where integrations take place.
Returns
-------
sma1, sma2 : float
The smaller and larger values of semimajor axis length that
define the annulus bounding ellipses.
"""
if (self.linear_growth):
a1 = self.sma - self.astep / 2.
a2 = self.sma + self.astep / 2.
else:
a1 = self.sma * (1. - self.astep / 2.)
a2 = self.sma * (1. + self.astep / 2.)
return a1, a2 | [
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.update_sma | def update_sma(self, step):
"""
Calculate an updated value for the semimajor axis, given the
current value and the step value.
The step value must be managed by the caller to support both
modes: grow outwards and shrink inwards.
Parameters
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The step value.
Returns
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The new semimajor axis length.
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return sma | python | def update_sma(self, step):
"""
Calculate an updated value for the semimajor axis, given the
current value and the step value.
The step value must be managed by the caller to support both
modes: grow outwards and shrink inwards.
Parameters
----------
step : float
The step value.
Returns
-------
sma : float
The new semimajor axis length.
"""
if self.linear_growth:
sma = self.sma + step
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sma = self.sma * (1. + step)
return sma | [
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Parameters
----------
step : float
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sma : float
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.reset_sma | def reset_sma(self, step):
"""
Change the direction of semimajor axis growth, from outwards to
inwards.
Parameters
----------
step : float
The current step value.
Returns
-------
sma, new_step : float
The new semimajor axis length and the new step value to
initiate the shrinking of the semimajor axis length. This is
the step value that should be used when calling the
:meth:`~photutils.isophote.EllipseGeometry.update_sma`
method.
"""
if self.linear_growth:
sma = self.sma - step
step = -step
else:
aux = 1. / (1. + step)
sma = self.sma * aux
step = aux - 1.
return sma, step | python | def reset_sma(self, step):
"""
Change the direction of semimajor axis growth, from outwards to
inwards.
Parameters
----------
step : float
The current step value.
Returns
-------
sma, new_step : float
The new semimajor axis length and the new step value to
initiate the shrinking of the semimajor axis length. This is
the step value that should be used when calling the
:meth:`~photutils.isophote.EllipseGeometry.update_sma`
method.
"""
if self.linear_growth:
sma = self.sma - step
step = -step
else:
aux = 1. / (1. + step)
sma = self.sma * aux
step = aux - 1.
return sma, step | [
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astropy/photutils | photutils/psf/matching/fourier.py | resize_psf | def resize_psf(psf, input_pixel_scale, output_pixel_scale, order=3):
"""
Resize a PSF using spline interpolation of the requested order.
Parameters
----------
psf : 2D `~numpy.ndarray`
The 2D data array of the PSF.
input_pixel_scale : float
The pixel scale of the input ``psf``. The units must
match ``output_pixel_scale``.
output_pixel_scale : float
The pixel scale of the output ``psf``. The units must
match ``input_pixel_scale``.
order : float, optional
The order of the spline interpolation (0-5). The default is 3.
Returns
-------
result : 2D `~numpy.ndarray`
The resampled/interpolated 2D data array.
"""
from scipy.ndimage import zoom
ratio = input_pixel_scale / output_pixel_scale
return zoom(psf, ratio, order=order) / ratio**2 | python | def resize_psf(psf, input_pixel_scale, output_pixel_scale, order=3):
"""
Resize a PSF using spline interpolation of the requested order.
Parameters
----------
psf : 2D `~numpy.ndarray`
The 2D data array of the PSF.
input_pixel_scale : float
The pixel scale of the input ``psf``. The units must
match ``output_pixel_scale``.
output_pixel_scale : float
The pixel scale of the output ``psf``. The units must
match ``input_pixel_scale``.
order : float, optional
The order of the spline interpolation (0-5). The default is 3.
Returns
-------
result : 2D `~numpy.ndarray`
The resampled/interpolated 2D data array.
"""
from scipy.ndimage import zoom
ratio = input_pixel_scale / output_pixel_scale
return zoom(psf, ratio, order=order) / ratio**2 | [
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astropy/photutils | photutils/background/background_2d.py | Background2D._select_meshes | def _select_meshes(self, data):
"""
Define the x and y indices with respect to the low-resolution
mesh image of the meshes to use for the background
interpolation.
The ``exclude_percentile`` keyword determines which meshes are
not used for the background interpolation.
Parameters
----------
data : 2D `~numpy.ma.MaskedArray`
A 2D array where the y dimension represents each mesh and
the x dimension represents the data in each mesh.
Returns
-------
mesh_idx : 1D `~numpy.ndarray`
The 1D mesh indices.
"""
# the number of masked pixels in each mesh
nmasked = np.ma.count_masked(data, axis=1)
# meshes that contain more than ``exclude_percentile`` percent
# masked pixels are excluded:
# - for exclude_percentile=0, good meshes will be only where
# nmasked=0
# - meshes where nmasked=self.box_npixels are *always* excluded
# (second conditional needed for exclude_percentile=100)
threshold_npixels = self.exclude_percentile / 100. * self.box_npixels
mesh_idx = np.where((nmasked <= threshold_npixels) &
(nmasked != self.box_npixels))[0] # good meshes
if len(mesh_idx) == 0:
raise ValueError('All meshes contain > {0} ({1} percent per '
'mesh) masked pixels. Please check your data '
'or decrease "exclude_percentile".'
.format(threshold_npixels,
self.exclude_percentile))
return mesh_idx | python | def _select_meshes(self, data):
"""
Define the x and y indices with respect to the low-resolution
mesh image of the meshes to use for the background
interpolation.
The ``exclude_percentile`` keyword determines which meshes are
not used for the background interpolation.
Parameters
----------
data : 2D `~numpy.ma.MaskedArray`
A 2D array where the y dimension represents each mesh and
the x dimension represents the data in each mesh.
Returns
-------
mesh_idx : 1D `~numpy.ndarray`
The 1D mesh indices.
"""
# the number of masked pixels in each mesh
nmasked = np.ma.count_masked(data, axis=1)
# meshes that contain more than ``exclude_percentile`` percent
# masked pixels are excluded:
# - for exclude_percentile=0, good meshes will be only where
# nmasked=0
# - meshes where nmasked=self.box_npixels are *always* excluded
# (second conditional needed for exclude_percentile=100)
threshold_npixels = self.exclude_percentile / 100. * self.box_npixels
mesh_idx = np.where((nmasked <= threshold_npixels) &
(nmasked != self.box_npixels))[0] # good meshes
if len(mesh_idx) == 0:
raise ValueError('All meshes contain > {0} ({1} percent per '
'mesh) masked pixels. Please check your data '
'or decrease "exclude_percentile".'
.format(threshold_npixels,
self.exclude_percentile))
return mesh_idx | [
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astropy/photutils | photutils/background/background_2d.py | Background2D._prepare_data | def _prepare_data(self):
"""
Prepare the data.
First, pad or crop the 2D data array so that there are an
integer number of meshes in both dimensions, creating a masked
array.
Then reshape into a different 2D masked array where each row
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"""
self.nyboxes = self.data.shape[0] // self.box_size[0]
self.nxboxes = self.data.shape[1] // self.box_size[1]
yextra = self.data.shape[0] % self.box_size[0]
xextra = self.data.shape[1] % self.box_size[1]
if (xextra + yextra) == 0:
# no resizing of the data is necessary
data_ma = np.ma.masked_array(self.data, mask=self.mask)
else:
# pad or crop the data
if self.edge_method == 'pad':
data_ma = self._pad_data(yextra, xextra)
self.nyboxes = data_ma.shape[0] // self.box_size[0]
self.nxboxes = data_ma.shape[1] // self.box_size[1]
elif self.edge_method == 'crop':
data_ma = self._crop_data()
else:
raise ValueError('edge_method must be "pad" or "crop"')
self.nboxes = self.nxboxes * self.nyboxes
# a reshaped 2D masked array with mesh data along the x axis
mesh_data = np.ma.swapaxes(data_ma.reshape(
self.nyboxes, self.box_size[0], self.nxboxes, self.box_size[1]),
1, 2).reshape(self.nyboxes * self.nxboxes, self.box_npixels)
# first cut on rejecting meshes
self.mesh_idx = self._select_meshes(mesh_data)
self._mesh_data = mesh_data[self.mesh_idx, :]
return | python | def _prepare_data(self):
"""
Prepare the data.
First, pad or crop the 2D data array so that there are an
integer number of meshes in both dimensions, creating a masked
array.
Then reshape into a different 2D masked array where each row
represents the data in a single mesh. This method also performs
a first cut at rejecting certain meshes as specified by the
input keywords.
"""
self.nyboxes = self.data.shape[0] // self.box_size[0]
self.nxboxes = self.data.shape[1] // self.box_size[1]
yextra = self.data.shape[0] % self.box_size[0]
xextra = self.data.shape[1] % self.box_size[1]
if (xextra + yextra) == 0:
# no resizing of the data is necessary
data_ma = np.ma.masked_array(self.data, mask=self.mask)
else:
# pad or crop the data
if self.edge_method == 'pad':
data_ma = self._pad_data(yextra, xextra)
self.nyboxes = data_ma.shape[0] // self.box_size[0]
self.nxboxes = data_ma.shape[1] // self.box_size[1]
elif self.edge_method == 'crop':
data_ma = self._crop_data()
else:
raise ValueError('edge_method must be "pad" or "crop"')
self.nboxes = self.nxboxes * self.nyboxes
# a reshaped 2D masked array with mesh data along the x axis
mesh_data = np.ma.swapaxes(data_ma.reshape(
self.nyboxes, self.box_size[0], self.nxboxes, self.box_size[1]),
1, 2).reshape(self.nyboxes * self.nxboxes, self.box_npixels)
# first cut on rejecting meshes
self.mesh_idx = self._select_meshes(mesh_data)
self._mesh_data = mesh_data[self.mesh_idx, :]
return | [
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astropy/photutils | photutils/background/background_2d.py | Background2D._make_2d_array | def _make_2d_array(self, data):
"""
Convert a 1D array of mesh values to a masked 2D mesh array
given the 1D mesh indices ``mesh_idx``.
Parameters
----------
data : 1D `~numpy.ndarray`
A 1D array of mesh values.
Returns
-------
result : 2D `~numpy.ma.MaskedArray`
A 2D masked array. Pixels not defined in ``mesh_idx`` are
masked.
"""
if data.shape != self.mesh_idx.shape:
raise ValueError('data and mesh_idx must have the same shape')
if np.ma.is_masked(data):
raise ValueError('data must not be a masked array')
data2d = np.zeros(self._mesh_shape).astype(data.dtype)
data2d[self.mesh_yidx, self.mesh_xidx] = data
if len(self.mesh_idx) == self.nboxes:
# no meshes were masked
return data2d
else:
# some meshes were masked
mask2d = np.ones(data2d.shape).astype(np.bool)
mask2d[self.mesh_yidx, self.mesh_xidx] = False
return np.ma.masked_array(data2d, mask=mask2d) | python | def _make_2d_array(self, data):
"""
Convert a 1D array of mesh values to a masked 2D mesh array
given the 1D mesh indices ``mesh_idx``.
Parameters
----------
data : 1D `~numpy.ndarray`
A 1D array of mesh values.
Returns
-------
result : 2D `~numpy.ma.MaskedArray`
A 2D masked array. Pixels not defined in ``mesh_idx`` are
masked.
"""
if data.shape != self.mesh_idx.shape:
raise ValueError('data and mesh_idx must have the same shape')
if np.ma.is_masked(data):
raise ValueError('data must not be a masked array')
data2d = np.zeros(self._mesh_shape).astype(data.dtype)
data2d[self.mesh_yidx, self.mesh_xidx] = data
if len(self.mesh_idx) == self.nboxes:
# no meshes were masked
return data2d
else:
# some meshes were masked
mask2d = np.ones(data2d.shape).astype(np.bool)
mask2d[self.mesh_yidx, self.mesh_xidx] = False
return np.ma.masked_array(data2d, mask=mask2d) | [
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A 1D array of mesh values.
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A 2D masked array. Pixels not defined in ``mesh_idx`` are
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astropy/photutils | photutils/background/background_2d.py | Background2D._interpolate_meshes | def _interpolate_meshes(self, data, n_neighbors=10, eps=0., power=1.,
reg=0.):
"""
Use IDW interpolation to fill in any masked pixels in the
low-resolution 2D mesh background and background RMS images.
This is required to use a regular-grid interpolator to expand
the low-resolution image to the full size image.
Parameters
----------
data : 1D `~numpy.ndarray`
A 1D array of mesh values.
n_neighbors : int, optional
The maximum number of nearest neighbors to use during the
interpolation.
eps : float, optional
Set to use approximate nearest neighbors; the kth neighbor
is guaranteed to be no further than (1 + ``eps``) times the
distance to the real *k*-th nearest neighbor. See
`scipy.spatial.cKDTree.query` for further information.
power : float, optional
The power of the inverse distance used for the interpolation
weights. See the Notes section for more details.
reg : float, optional
The regularization parameter. It may be used to control the
smoothness of the interpolator. See the Notes section for
more details.
Returns
-------
result : 2D `~numpy.ndarray`
A 2D array of the mesh values where masked pixels have been
filled by IDW interpolation.
"""
yx = np.column_stack([self.mesh_yidx, self.mesh_xidx])
coords = np.array(list(product(range(self.nyboxes),
range(self.nxboxes))))
f = ShepardIDWInterpolator(yx, data)
img1d = f(coords, n_neighbors=n_neighbors, power=power, eps=eps,
reg=reg)
return img1d.reshape(self._mesh_shape) | python | def _interpolate_meshes(self, data, n_neighbors=10, eps=0., power=1.,
reg=0.):
"""
Use IDW interpolation to fill in any masked pixels in the
low-resolution 2D mesh background and background RMS images.
This is required to use a regular-grid interpolator to expand
the low-resolution image to the full size image.
Parameters
----------
data : 1D `~numpy.ndarray`
A 1D array of mesh values.
n_neighbors : int, optional
The maximum number of nearest neighbors to use during the
interpolation.
eps : float, optional
Set to use approximate nearest neighbors; the kth neighbor
is guaranteed to be no further than (1 + ``eps``) times the
distance to the real *k*-th nearest neighbor. See
`scipy.spatial.cKDTree.query` for further information.
power : float, optional
The power of the inverse distance used for the interpolation
weights. See the Notes section for more details.
reg : float, optional
The regularization parameter. It may be used to control the
smoothness of the interpolator. See the Notes section for
more details.
Returns
-------
result : 2D `~numpy.ndarray`
A 2D array of the mesh values where masked pixels have been
filled by IDW interpolation.
"""
yx = np.column_stack([self.mesh_yidx, self.mesh_xidx])
coords = np.array(list(product(range(self.nyboxes),
range(self.nxboxes))))
f = ShepardIDWInterpolator(yx, data)
img1d = f(coords, n_neighbors=n_neighbors, power=power, eps=eps,
reg=reg)
return img1d.reshape(self._mesh_shape) | [
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A 1D array of mesh values.
n_neighbors : int, optional
The maximum number of nearest neighbors to use during the
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eps : float, optional
Set to use approximate nearest neighbors; the kth neighbor
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The regularization parameter. It may be used to control the
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A 2D array of the mesh values where masked pixels have been
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astropy/photutils | photutils/background/background_2d.py | Background2D._selective_filter | def _selective_filter(self, data, indices):
"""
Selectively filter only pixels above ``filter_threshold`` in the
background mesh.
The same pixels are filtered in both the background and
background RMS meshes.
Parameters
----------
data : 2D `~numpy.ndarray`
A 2D array of mesh values.
indices : 2 tuple of int
A tuple of the ``y`` and ``x`` indices of the pixels to
filter.
Returns
-------
filtered_data : 2D `~numpy.ndarray`
The filtered 2D array of mesh values.
"""
data_out = np.copy(data)
for i, j in zip(*indices):
yfs, xfs = self.filter_size
hyfs, hxfs = yfs // 2, xfs // 2
y0, y1 = max(i - hyfs, 0), min(i - hyfs + yfs, data.shape[0])
x0, x1 = max(j - hxfs, 0), min(j - hxfs + xfs, data.shape[1])
data_out[i, j] = np.median(data[y0:y1, x0:x1])
return data_out | python | def _selective_filter(self, data, indices):
"""
Selectively filter only pixels above ``filter_threshold`` in the
background mesh.
The same pixels are filtered in both the background and
background RMS meshes.
Parameters
----------
data : 2D `~numpy.ndarray`
A 2D array of mesh values.
indices : 2 tuple of int
A tuple of the ``y`` and ``x`` indices of the pixels to
filter.
Returns
-------
filtered_data : 2D `~numpy.ndarray`
The filtered 2D array of mesh values.
"""
data_out = np.copy(data)
for i, j in zip(*indices):
yfs, xfs = self.filter_size
hyfs, hxfs = yfs // 2, xfs // 2
y0, y1 = max(i - hyfs, 0), min(i - hyfs + yfs, data.shape[0])
x0, x1 = max(j - hxfs, 0), min(j - hxfs + xfs, data.shape[1])
data_out[i, j] = np.median(data[y0:y1, x0:x1])
return data_out | [
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A 2D array of mesh values.
indices : 2 tuple of int
A tuple of the ``y`` and ``x`` indices of the pixels to
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-------
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astropy/photutils | photutils/background/background_2d.py | Background2D._filter_meshes | def _filter_meshes(self):
"""
Apply a 2D median filter to the low-resolution 2D mesh,
including only pixels inside the image at the borders.
"""
from scipy.ndimage import generic_filter
try:
nanmedian_func = np.nanmedian # numpy >= 1.9
except AttributeError: # pragma: no cover
from scipy.stats import nanmedian
nanmedian_func = nanmedian
if self.filter_threshold is None:
# filter the entire arrays
self.background_mesh = generic_filter(
self.background_mesh, nanmedian_func, size=self.filter_size,
mode='constant', cval=np.nan)
self.background_rms_mesh = generic_filter(
self.background_rms_mesh, nanmedian_func,
size=self.filter_size, mode='constant', cval=np.nan)
else:
# selectively filter
indices = np.nonzero(self.background_mesh > self.filter_threshold)
self.background_mesh = self._selective_filter(
self.background_mesh, indices)
self.background_rms_mesh = self._selective_filter(
self.background_rms_mesh, indices)
return | python | def _filter_meshes(self):
"""
Apply a 2D median filter to the low-resolution 2D mesh,
including only pixels inside the image at the borders.
"""
from scipy.ndimage import generic_filter
try:
nanmedian_func = np.nanmedian # numpy >= 1.9
except AttributeError: # pragma: no cover
from scipy.stats import nanmedian
nanmedian_func = nanmedian
if self.filter_threshold is None:
# filter the entire arrays
self.background_mesh = generic_filter(
self.background_mesh, nanmedian_func, size=self.filter_size,
mode='constant', cval=np.nan)
self.background_rms_mesh = generic_filter(
self.background_rms_mesh, nanmedian_func,
size=self.filter_size, mode='constant', cval=np.nan)
else:
# selectively filter
indices = np.nonzero(self.background_mesh > self.filter_threshold)
self.background_mesh = self._selective_filter(
self.background_mesh, indices)
self.background_rms_mesh = self._selective_filter(
self.background_rms_mesh, indices)
return | [
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astropy/photutils | photutils/background/background_2d.py | Background2D._calc_bkg_bkgrms | def _calc_bkg_bkgrms(self):
"""
Calculate the background and background RMS estimate in each of
the meshes.
Both meshes are computed at the same time here method because
the filtering of both depends on the background mesh.
The ``background_mesh`` and ``background_rms_mesh`` images are
equivalent to the low-resolution "MINIBACKGROUND" and
"MINIBACK_RMS" background maps in SExtractor, respectively.
"""
if self.sigma_clip is not None:
data_sigclip = self.sigma_clip(self._mesh_data, axis=1)
else:
data_sigclip = self._mesh_data
del self._mesh_data
# preform mesh rejection on sigma-clipped data (i.e. for any
# newly-masked pixels)
idx = self._select_meshes(data_sigclip)
self.mesh_idx = self.mesh_idx[idx] # indices for the output mesh
self._data_sigclip = data_sigclip[idx] # always a 2D masked array
self._mesh_shape = (self.nyboxes, self.nxboxes)
self.mesh_yidx, self.mesh_xidx = np.unravel_index(self.mesh_idx,
self._mesh_shape)
# These properties are needed later to calculate
# background_mesh_ma and background_rms_mesh_ma. Note that _bkg1d
# and _bkgrms1d are masked arrays, but the mask should always be
# False.
self._bkg1d = self.bkg_estimator(self._data_sigclip, axis=1)
self._bkgrms1d = self.bkgrms_estimator(self._data_sigclip, axis=1)
# make the unfiltered 2D mesh arrays (these are not masked)
if len(self._bkg1d) == self.nboxes:
bkg = self._make_2d_array(self._bkg1d)
bkgrms = self._make_2d_array(self._bkgrms1d)
else:
bkg = self._interpolate_meshes(self._bkg1d)
bkgrms = self._interpolate_meshes(self._bkgrms1d)
self._background_mesh_unfiltered = bkg
self._background_rms_mesh_unfiltered = bkgrms
self.background_mesh = bkg
self.background_rms_mesh = bkgrms
# filter the 2D mesh arrays
if not np.array_equal(self.filter_size, [1, 1]):
self._filter_meshes()
return | python | def _calc_bkg_bkgrms(self):
"""
Calculate the background and background RMS estimate in each of
the meshes.
Both meshes are computed at the same time here method because
the filtering of both depends on the background mesh.
The ``background_mesh`` and ``background_rms_mesh`` images are
equivalent to the low-resolution "MINIBACKGROUND" and
"MINIBACK_RMS" background maps in SExtractor, respectively.
"""
if self.sigma_clip is not None:
data_sigclip = self.sigma_clip(self._mesh_data, axis=1)
else:
data_sigclip = self._mesh_data
del self._mesh_data
# preform mesh rejection on sigma-clipped data (i.e. for any
# newly-masked pixels)
idx = self._select_meshes(data_sigclip)
self.mesh_idx = self.mesh_idx[idx] # indices for the output mesh
self._data_sigclip = data_sigclip[idx] # always a 2D masked array
self._mesh_shape = (self.nyboxes, self.nxboxes)
self.mesh_yidx, self.mesh_xidx = np.unravel_index(self.mesh_idx,
self._mesh_shape)
# These properties are needed later to calculate
# background_mesh_ma and background_rms_mesh_ma. Note that _bkg1d
# and _bkgrms1d are masked arrays, but the mask should always be
# False.
self._bkg1d = self.bkg_estimator(self._data_sigclip, axis=1)
self._bkgrms1d = self.bkgrms_estimator(self._data_sigclip, axis=1)
# make the unfiltered 2D mesh arrays (these are not masked)
if len(self._bkg1d) == self.nboxes:
bkg = self._make_2d_array(self._bkg1d)
bkgrms = self._make_2d_array(self._bkgrms1d)
else:
bkg = self._interpolate_meshes(self._bkg1d)
bkgrms = self._interpolate_meshes(self._bkgrms1d)
self._background_mesh_unfiltered = bkg
self._background_rms_mesh_unfiltered = bkgrms
self.background_mesh = bkg
self.background_rms_mesh = bkgrms
# filter the 2D mesh arrays
if not np.array_equal(self.filter_size, [1, 1]):
self._filter_meshes()
return | [
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Both meshes are computed at the same time here method because
the filtering of both depends on the background mesh.
The ``background_mesh`` and ``background_rms_mesh`` images are
equivalent to the low-resolution "MINIBACKGROUND" and
"MINIBACK_RMS" background maps in SExtractor, respectively. | [
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astropy/photutils | photutils/background/background_2d.py | Background2D._calc_coordinates | def _calc_coordinates(self):
"""
Calculate the coordinates to use when calling an interpolator.
These are needed for `Background2D` and `BackgroundIDW2D`.
Regular-grid interpolators require a 2D array of values. Some
require a 2D meshgrid of x and y. Other require a strictly
increasing 1D array of the x and y ranges.
"""
# the position coordinates used to initialize an interpolation
self.y = (self.mesh_yidx * self.box_size[0] +
(self.box_size[0] - 1) / 2.)
self.x = (self.mesh_xidx * self.box_size[1] +
(self.box_size[1] - 1) / 2.)
self.yx = np.column_stack([self.y, self.x])
# the position coordinates used when calling an interpolator
nx, ny = self.data.shape
self.data_coords = np.array(list(product(range(ny), range(nx)))) | python | def _calc_coordinates(self):
"""
Calculate the coordinates to use when calling an interpolator.
These are needed for `Background2D` and `BackgroundIDW2D`.
Regular-grid interpolators require a 2D array of values. Some
require a 2D meshgrid of x and y. Other require a strictly
increasing 1D array of the x and y ranges.
"""
# the position coordinates used to initialize an interpolation
self.y = (self.mesh_yidx * self.box_size[0] +
(self.box_size[0] - 1) / 2.)
self.x = (self.mesh_xidx * self.box_size[1] +
(self.box_size[1] - 1) / 2.)
self.yx = np.column_stack([self.y, self.x])
# the position coordinates used when calling an interpolator
nx, ny = self.data.shape
self.data_coords = np.array(list(product(range(ny), range(nx)))) | [
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astropy/photutils | photutils/background/background_2d.py | Background2D.plot_meshes | def plot_meshes(self, ax=None, marker='+', color='blue', outlines=False,
**kwargs):
"""
Plot the low-resolution mesh boxes on a matplotlib Axes
instance.
Parameters
----------
ax : `matplotlib.axes.Axes` instance, optional
If `None`, then the current ``Axes`` instance is used.
marker : str, optional
The marker to use to mark the center of the boxes. Default
is '+'.
color : str, optional
The color for the markers and the box outlines. Default is
'blue'.
outlines : bool, optional
Whether or not to plot the box outlines in addition to the
box centers.
kwargs
Any keyword arguments accepted by
`matplotlib.patches.Patch`. Used only if ``outlines`` is
True.
"""
import matplotlib.pyplot as plt
kwargs['color'] = color
if ax is None:
ax = plt.gca()
ax.scatter(self.x, self.y, marker=marker, color=color)
if outlines:
from ..aperture import RectangularAperture
xy = np.column_stack([self.x, self.y])
apers = RectangularAperture(xy, self.box_size[1],
self.box_size[0], 0.)
apers.plot(ax=ax, **kwargs)
return | python | def plot_meshes(self, ax=None, marker='+', color='blue', outlines=False,
**kwargs):
"""
Plot the low-resolution mesh boxes on a matplotlib Axes
instance.
Parameters
----------
ax : `matplotlib.axes.Axes` instance, optional
If `None`, then the current ``Axes`` instance is used.
marker : str, optional
The marker to use to mark the center of the boxes. Default
is '+'.
color : str, optional
The color for the markers and the box outlines. Default is
'blue'.
outlines : bool, optional
Whether or not to plot the box outlines in addition to the
box centers.
kwargs
Any keyword arguments accepted by
`matplotlib.patches.Patch`. Used only if ``outlines`` is
True.
"""
import matplotlib.pyplot as plt
kwargs['color'] = color
if ax is None:
ax = plt.gca()
ax.scatter(self.x, self.y, marker=marker, color=color)
if outlines:
from ..aperture import RectangularAperture
xy = np.column_stack([self.x, self.y])
apers = RectangularAperture(xy, self.box_size[1],
self.box_size[0], 0.)
apers.plot(ax=ax, **kwargs)
return | [
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marker : str, optional
The marker to use to mark the center of the boxes. Default
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color : str, optional
The color for the markers and the box outlines. Default is
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outlines : bool, optional
Whether or not to plot the box outlines in addition to the
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kwargs
Any keyword arguments accepted by
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astropy/photutils | photutils/isophote/sample.py | EllipseSample.extract | def extract(self):
"""
Extract sample data by scanning an elliptical path over the
image array.
Returns
-------
result : 2D `~numpy.ndarray`
The rows of the array contain the angles, radii, and
extracted intensity values, respectively.
"""
# the sample values themselves are kept cached to prevent
# multiple calls to the integrator code.
if self.values is not None:
return self.values
else:
s = self._extract()
self.values = s
return s | python | def extract(self):
"""
Extract sample data by scanning an elliptical path over the
image array.
Returns
-------
result : 2D `~numpy.ndarray`
The rows of the array contain the angles, radii, and
extracted intensity values, respectively.
"""
# the sample values themselves are kept cached to prevent
# multiple calls to the integrator code.
if self.values is not None:
return self.values
else:
s = self._extract()
self.values = s
return s | [
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astropy/photutils | photutils/isophote/sample.py | EllipseSample.update | def update(self):
"""
Update this `~photutils.isophote.EllipseSample` instance.
This method calls the
:meth:`~photutils.isophote.EllipseSample.extract` method to get
the values that match the current ``geometry`` attribute, and
then computes the the mean intensity, local gradient, and other
associated quantities.
"""
step = self.geometry.astep
# Update the mean value first, using extraction from main sample.
s = self.extract()
self.mean = np.mean(s[2])
# Get sample with same geometry but at a different distance from
# center. Estimate gradient from there.
gradient, gradient_error = self._get_gradient(step)
# Check for meaningful gradient. If no meaningful gradient, try
# another sample, this time using larger radius. Meaningful
# gradient means something shallower, but still close to within
# a factor 3 from previous gradient estimate. If no previous
# estimate is available, guess it.
previous_gradient = self.gradient
if not previous_gradient:
previous_gradient = -0.05 # good enough, based on usage
if gradient >= (previous_gradient / 3.): # gradient is negative!
gradient, gradient_error = self._get_gradient(2 * step)
# If still no meaningful gradient can be measured, try with
# previous one, slightly shallower. A factor 0.8 is not too far
# from what is expected from geometrical sampling steps of 10-20%
# and a deVaucouleurs law or an exponential disk (at least at its
# inner parts, r <~ 5 req). Gradient error is meaningless in this
# case.
if gradient >= (previous_gradient / 3.):
gradient = previous_gradient * 0.8
gradient_error = None
self.gradient = gradient
self.gradient_error = gradient_error
if gradient_error:
self.gradient_relative_error = gradient_error / np.abs(gradient)
else:
self.gradient_relative_error = None | python | def update(self):
"""
Update this `~photutils.isophote.EllipseSample` instance.
This method calls the
:meth:`~photutils.isophote.EllipseSample.extract` method to get
the values that match the current ``geometry`` attribute, and
then computes the the mean intensity, local gradient, and other
associated quantities.
"""
step = self.geometry.astep
# Update the mean value first, using extraction from main sample.
s = self.extract()
self.mean = np.mean(s[2])
# Get sample with same geometry but at a different distance from
# center. Estimate gradient from there.
gradient, gradient_error = self._get_gradient(step)
# Check for meaningful gradient. If no meaningful gradient, try
# another sample, this time using larger radius. Meaningful
# gradient means something shallower, but still close to within
# a factor 3 from previous gradient estimate. If no previous
# estimate is available, guess it.
previous_gradient = self.gradient
if not previous_gradient:
previous_gradient = -0.05 # good enough, based on usage
if gradient >= (previous_gradient / 3.): # gradient is negative!
gradient, gradient_error = self._get_gradient(2 * step)
# If still no meaningful gradient can be measured, try with
# previous one, slightly shallower. A factor 0.8 is not too far
# from what is expected from geometrical sampling steps of 10-20%
# and a deVaucouleurs law or an exponential disk (at least at its
# inner parts, r <~ 5 req). Gradient error is meaningless in this
# case.
if gradient >= (previous_gradient / 3.):
gradient = previous_gradient * 0.8
gradient_error = None
self.gradient = gradient
self.gradient_error = gradient_error
if gradient_error:
self.gradient_relative_error = gradient_error / np.abs(gradient)
else:
self.gradient_relative_error = None | [
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astropy/photutils | photutils/isophote/fitter.py | EllipseFitter.fit | def fit(self, conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT,
maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG, maxgerr=DEFAULT_MAXGERR,
going_inwards=False):
"""
Fit an elliptical isophote.
Parameters
----------
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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.
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 in the
`~photutils.isophote.EllipseSample` class). The default is
`False`.
Returns
-------
result : `~photutils.isophote.Isophote` instance
The fitted isophote, which also contains fit status
information.
Examples
--------
>>> from photutils.isophote import EllipseSample, EllipseFitter
>>> sample = EllipseSample(data, sma=10.)
>>> fitter = EllipseFitter(sample)
>>> isophote = fitter.fit()
"""
sample = self._sample
# this flag signals that limiting gradient error (`maxgerr`)
# wasn't exceeded yet.
lexceed = False
# here we keep track of the sample that caused the minimum harmonic
# amplitude(in absolute value). This will eventually be used to
# build the resulting Isophote in cases where iterations run to
# the maximum allowed (maxit), or the maximum number of flagged
# data points (fflag) is reached.
minimum_amplitude_value = np.Inf
minimum_amplitude_sample = None
for iter in range(maxit):
# Force the sample to compute its gradient and associated values.
sample.update()
# The extract() method returns sampled values as a 2-d numpy array
# with the following structure:
# values[0] = 1-d array with angles
# values[1] = 1-d array with radii
# values[2] = 1-d array with intensity
values = sample.extract()
# Fit harmonic coefficients. Failure in fitting is
# a fatal error; terminate immediately with sample
# marked as invalid.
try:
coeffs = fit_first_and_second_harmonics(values[0], values[2])
except Exception as e:
log.info(e)
return Isophote(sample, iter+1, False, 3)
coeffs = coeffs[0]
# largest harmonic in absolute value drives the correction.
largest_harmonic_index = np.argmax(np.abs(coeffs[1:]))
largest_harmonic = coeffs[1:][largest_harmonic_index]
# see if the amplitude decreased; if yes, keep the
# corresponding sample for eventual later use.
if abs(largest_harmonic) < minimum_amplitude_value:
minimum_amplitude_value = abs(largest_harmonic)
minimum_amplitude_sample = sample
# check if converged
model = first_and_second_harmonic_function(values[0], coeffs)
residual = values[2] - model
if ((conver * sample.sector_area * np.std(residual))
> np.abs(largest_harmonic)):
# Got a valid solution. But before returning, ensure
# that a minimum of iterations has run.
if iter >= minit-1:
sample.update()
return Isophote(sample, iter+1, True, 0)
# it may not have converged yet, but the sample contains too
# many invalid data points: return.
if sample.actual_points < (sample.total_points * fflag):
# when too many data points were flagged, return the
# best fit sample instead of the current one.
minimum_amplitude_sample.update()
return Isophote(minimum_amplitude_sample, iter+1, True, 1)
# pick appropriate corrector code.
corrector = _correctors[largest_harmonic_index]
# generate *NEW* EllipseSample instance with corrected
# parameter. Note that this instance is still devoid of other
# information besides its geometry. It needs to be explicitly
# updated for computations to proceed. We have to build a new
# EllipseSample instance every time because of the lazy
# extraction process used by EllipseSample code. To minimize
# the number of calls to the area integrators, we pay a
# (hopefully smaller) price here, by having multiple calls to
# the EllipseSample constructor.
sample = corrector.correct(sample, largest_harmonic)
sample.update()
# see if any abnormal (or unusual) conditions warrant
# the change to non-iterative mode, or go-inwards mode.
proceed, lexceed = self._check_conditions(
sample, maxgerr, going_inwards, lexceed)
if not proceed:
sample.update()
return Isophote(sample, iter+1, True, -1)
# Got to the maximum number of iterations. Return with
# code 2, and handle it as a valid isophote. Use the
# best fit sample instead of the current one.
minimum_amplitude_sample.update()
return Isophote(minimum_amplitude_sample, maxit, True, 2) | python | def fit(self, conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT,
maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG, maxgerr=DEFAULT_MAXGERR,
going_inwards=False):
"""
Fit an elliptical isophote.
Parameters
----------
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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.
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 in the
`~photutils.isophote.EllipseSample` class). The default is
`False`.
Returns
-------
result : `~photutils.isophote.Isophote` instance
The fitted isophote, which also contains fit status
information.
Examples
--------
>>> from photutils.isophote import EllipseSample, EllipseFitter
>>> sample = EllipseSample(data, sma=10.)
>>> fitter = EllipseFitter(sample)
>>> isophote = fitter.fit()
"""
sample = self._sample
# this flag signals that limiting gradient error (`maxgerr`)
# wasn't exceeded yet.
lexceed = False
# here we keep track of the sample that caused the minimum harmonic
# amplitude(in absolute value). This will eventually be used to
# build the resulting Isophote in cases where iterations run to
# the maximum allowed (maxit), or the maximum number of flagged
# data points (fflag) is reached.
minimum_amplitude_value = np.Inf
minimum_amplitude_sample = None
for iter in range(maxit):
# Force the sample to compute its gradient and associated values.
sample.update()
# The extract() method returns sampled values as a 2-d numpy array
# with the following structure:
# values[0] = 1-d array with angles
# values[1] = 1-d array with radii
# values[2] = 1-d array with intensity
values = sample.extract()
# Fit harmonic coefficients. Failure in fitting is
# a fatal error; terminate immediately with sample
# marked as invalid.
try:
coeffs = fit_first_and_second_harmonics(values[0], values[2])
except Exception as e:
log.info(e)
return Isophote(sample, iter+1, False, 3)
coeffs = coeffs[0]
# largest harmonic in absolute value drives the correction.
largest_harmonic_index = np.argmax(np.abs(coeffs[1:]))
largest_harmonic = coeffs[1:][largest_harmonic_index]
# see if the amplitude decreased; if yes, keep the
# corresponding sample for eventual later use.
if abs(largest_harmonic) < minimum_amplitude_value:
minimum_amplitude_value = abs(largest_harmonic)
minimum_amplitude_sample = sample
# check if converged
model = first_and_second_harmonic_function(values[0], coeffs)
residual = values[2] - model
if ((conver * sample.sector_area * np.std(residual))
> np.abs(largest_harmonic)):
# Got a valid solution. But before returning, ensure
# that a minimum of iterations has run.
if iter >= minit-1:
sample.update()
return Isophote(sample, iter+1, True, 0)
# it may not have converged yet, but the sample contains too
# many invalid data points: return.
if sample.actual_points < (sample.total_points * fflag):
# when too many data points were flagged, return the
# best fit sample instead of the current one.
minimum_amplitude_sample.update()
return Isophote(minimum_amplitude_sample, iter+1, True, 1)
# pick appropriate corrector code.
corrector = _correctors[largest_harmonic_index]
# generate *NEW* EllipseSample instance with corrected
# parameter. Note that this instance is still devoid of other
# information besides its geometry. It needs to be explicitly
# updated for computations to proceed. We have to build a new
# EllipseSample instance every time because of the lazy
# extraction process used by EllipseSample code. To minimize
# the number of calls to the area integrators, we pay a
# (hopefully smaller) price here, by having multiple calls to
# the EllipseSample constructor.
sample = corrector.correct(sample, largest_harmonic)
sample.update()
# see if any abnormal (or unusual) conditions warrant
# the change to non-iterative mode, or go-inwards mode.
proceed, lexceed = self._check_conditions(
sample, maxgerr, going_inwards, lexceed)
if not proceed:
sample.update()
return Isophote(sample, iter+1, True, -1)
# Got to the maximum number of iterations. Return with
# code 2, and handle it as a valid isophote. Use the
# best fit sample instead of the current one.
minimum_amplitude_sample.update()
return Isophote(minimum_amplitude_sample, maxit, True, 2) | [
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Parameters
----------
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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.
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 in the
`~photutils.isophote.EllipseSample` class). The default is
`False`.
Returns
-------
result : `~photutils.isophote.Isophote` instance
The fitted isophote, which also contains fit status
information.
Examples
--------
>>> from photutils.isophote import EllipseSample, EllipseFitter
>>> sample = EllipseSample(data, sma=10.)
>>> fitter = EllipseFitter(sample)
>>> isophote = fitter.fit() | [
"Fit",
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"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/isophote/fitter.py#L41-L217 | train |
astropy/photutils | photutils/psf/epsf_stars.py | _extract_stars | def _extract_stars(data, catalog, size=(11, 11), use_xy=True):
"""
Extract cutout images from a single image centered on stars defined
in the single input catalog.
Parameters
----------
data : `~astropy.nddata.NDData`
A `~astropy.nddata.NDData` object containing the 2D image from
which to extract the stars. If the input ``catalog`` contains
only the sky coordinates (i.e. not the pixel coordinates) of the
stars then the `~astropy.nddata.NDData` object must have a valid
``wcs`` attribute.
catalogs : `~astropy.table.Table`
A single catalog of sources to be extracted from the input
``data``. The center of each source can be defined either in
pixel coordinates (in ``x`` and ``y`` columns) or sky
coordinates (in a ``skycoord`` column containing a
`~astropy.coordinates.SkyCoord` object). If both are specified,
then the value of the ``use_xy`` keyword determines which
coordinates will be used.
size : int or array_like (int), optional
The extraction box size along each axis. If ``size`` is a
scalar then a square box of size ``size`` will be used. If
``size`` has two elements, they should be in ``(ny, nx)`` order.
The size must be greater than or equal to 3 pixel for both axes.
use_xy : bool, optional
Whether to use the ``x`` and ``y`` pixel positions when both
pixel and sky coordinates are present in the input catalog
table. If `False` then sky coordinates are used instead of
pixel coordinates (e.g. for linked stars). The default is
`True`.
Returns
-------
stars : list of `EPSFStar` objects
A list of `EPSFStar` instances containing the extracted stars.
"""
colnames = catalog.colnames
if ('x' not in colnames or 'y' not in colnames) or not use_xy:
xcenters, ycenters = skycoord_to_pixel(catalog['skycoord'], data.wcs,
origin=0, mode='all')
else:
xcenters = catalog['x'].data.astype(np.float)
ycenters = catalog['y'].data.astype(np.float)
if 'id' in colnames:
ids = catalog['id']
else:
ids = np.arange(len(catalog), dtype=np.int) + 1
if data.uncertainty is None:
weights = np.ones_like(data.data)
else:
if data.uncertainty.uncertainty_type == 'weights':
weights = np.asanyarray(data.uncertainty.array, dtype=np.float)
else:
warnings.warn('The data uncertainty attribute has an unsupported '
'type. Only uncertainty_type="weights" can be '
'used to set weights. Weights will be set to 1.',
AstropyUserWarning)
weights = np.ones_like(data.data)
if data.mask is not None:
weights[data.mask] = 0.
stars = []
for xcenter, ycenter, obj_id in zip(xcenters, ycenters, ids):
try:
large_slc, small_slc = overlap_slices(data.data.shape, size,
(ycenter, xcenter),
mode='strict')
data_cutout = data.data[large_slc]
weights_cutout = weights[large_slc]
except (PartialOverlapError, NoOverlapError):
stars.append(None)
continue
origin = (large_slc[1].start, large_slc[0].start)
cutout_center = (xcenter - origin[0], ycenter - origin[1])
star = EPSFStar(data_cutout, weights_cutout,
cutout_center=cutout_center, origin=origin,
wcs_large=data.wcs, id_label=obj_id)
stars.append(star)
return stars | python | def _extract_stars(data, catalog, size=(11, 11), use_xy=True):
"""
Extract cutout images from a single image centered on stars defined
in the single input catalog.
Parameters
----------
data : `~astropy.nddata.NDData`
A `~astropy.nddata.NDData` object containing the 2D image from
which to extract the stars. If the input ``catalog`` contains
only the sky coordinates (i.e. not the pixel coordinates) of the
stars then the `~astropy.nddata.NDData` object must have a valid
``wcs`` attribute.
catalogs : `~astropy.table.Table`
A single catalog of sources to be extracted from the input
``data``. The center of each source can be defined either in
pixel coordinates (in ``x`` and ``y`` columns) or sky
coordinates (in a ``skycoord`` column containing a
`~astropy.coordinates.SkyCoord` object). If both are specified,
then the value of the ``use_xy`` keyword determines which
coordinates will be used.
size : int or array_like (int), optional
The extraction box size along each axis. If ``size`` is a
scalar then a square box of size ``size`` will be used. If
``size`` has two elements, they should be in ``(ny, nx)`` order.
The size must be greater than or equal to 3 pixel for both axes.
use_xy : bool, optional
Whether to use the ``x`` and ``y`` pixel positions when both
pixel and sky coordinates are present in the input catalog
table. If `False` then sky coordinates are used instead of
pixel coordinates (e.g. for linked stars). The default is
`True`.
Returns
-------
stars : list of `EPSFStar` objects
A list of `EPSFStar` instances containing the extracted stars.
"""
colnames = catalog.colnames
if ('x' not in colnames or 'y' not in colnames) or not use_xy:
xcenters, ycenters = skycoord_to_pixel(catalog['skycoord'], data.wcs,
origin=0, mode='all')
else:
xcenters = catalog['x'].data.astype(np.float)
ycenters = catalog['y'].data.astype(np.float)
if 'id' in colnames:
ids = catalog['id']
else:
ids = np.arange(len(catalog), dtype=np.int) + 1
if data.uncertainty is None:
weights = np.ones_like(data.data)
else:
if data.uncertainty.uncertainty_type == 'weights':
weights = np.asanyarray(data.uncertainty.array, dtype=np.float)
else:
warnings.warn('The data uncertainty attribute has an unsupported '
'type. Only uncertainty_type="weights" can be '
'used to set weights. Weights will be set to 1.',
AstropyUserWarning)
weights = np.ones_like(data.data)
if data.mask is not None:
weights[data.mask] = 0.
stars = []
for xcenter, ycenter, obj_id in zip(xcenters, ycenters, ids):
try:
large_slc, small_slc = overlap_slices(data.data.shape, size,
(ycenter, xcenter),
mode='strict')
data_cutout = data.data[large_slc]
weights_cutout = weights[large_slc]
except (PartialOverlapError, NoOverlapError):
stars.append(None)
continue
origin = (large_slc[1].start, large_slc[0].start)
cutout_center = (xcenter - origin[0], ycenter - origin[1])
star = EPSFStar(data_cutout, weights_cutout,
cutout_center=cutout_center, origin=origin,
wcs_large=data.wcs, id_label=obj_id)
stars.append(star)
return stars | [
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Parameters
----------
data : `~astropy.nddata.NDData`
A `~astropy.nddata.NDData` object containing the 2D image from
which to extract the stars. If the input ``catalog`` contains
only the sky coordinates (i.e. not the pixel coordinates) of the
stars then the `~astropy.nddata.NDData` object must have a valid
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catalogs : `~astropy.table.Table`
A single catalog of sources to be extracted from the input
``data``. The center of each source can be defined either in
pixel coordinates (in ``x`` and ``y`` columns) or sky
coordinates (in a ``skycoord`` column containing a
`~astropy.coordinates.SkyCoord` object). If both are specified,
then the value of the ``use_xy`` keyword determines which
coordinates will be used.
size : int or array_like (int), optional
The extraction box size along each axis. If ``size`` is a
scalar then a square box of size ``size`` will be used. If
``size`` has two elements, they should be in ``(ny, nx)`` order.
The size must be greater than or equal to 3 pixel for both axes.
use_xy : bool, optional
Whether to use the ``x`` and ``y`` pixel positions when both
pixel and sky coordinates are present in the input catalog
table. If `False` then sky coordinates are used instead of
pixel coordinates (e.g. for linked stars). The default is
`True`.
Returns
-------
stars : list of `EPSFStar` objects
A list of `EPSFStar` instances containing the extracted stars. | [
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astropy/photutils | photutils/psf/epsf_stars.py | EPSFStar.estimate_flux | def estimate_flux(self):
"""
Estimate the star's flux by summing values in the input cutout
array.
Missing data is filled in by interpolation to better estimate
the total flux.
"""
from .epsf import _interpolate_missing_data
if np.any(self.mask):
data_interp = _interpolate_missing_data(self.data, method='cubic',
mask=self.mask)
data_interp = _interpolate_missing_data(data_interp,
method='nearest',
mask=self.mask)
flux = np.sum(data_interp, dtype=np.float64)
else:
flux = np.sum(self.data, dtype=np.float64)
return flux | python | def estimate_flux(self):
"""
Estimate the star's flux by summing values in the input cutout
array.
Missing data is filled in by interpolation to better estimate
the total flux.
"""
from .epsf import _interpolate_missing_data
if np.any(self.mask):
data_interp = _interpolate_missing_data(self.data, method='cubic',
mask=self.mask)
data_interp = _interpolate_missing_data(data_interp,
method='nearest',
mask=self.mask)
flux = np.sum(data_interp, dtype=np.float64)
else:
flux = np.sum(self.data, dtype=np.float64)
return flux | [
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astropy/photutils | photutils/psf/epsf_stars.py | EPSFStar._xy_idx | def _xy_idx(self):
"""
1D arrays of x and y indices of unmasked pixels in the cutout
reference frame.
"""
yidx, xidx = np.indices(self._data.shape)
return xidx[~self.mask].ravel(), yidx[~self.mask].ravel() | python | def _xy_idx(self):
"""
1D arrays of x and y indices of unmasked pixels in the cutout
reference frame.
"""
yidx, xidx = np.indices(self._data.shape)
return xidx[~self.mask].ravel(), yidx[~self.mask].ravel() | [
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astropy/photutils | photutils/psf/groupstars.py | DAOGroup.find_group | def find_group(self, star, starlist):
"""
Find the ids of those stars in ``starlist`` which are at a
distance less than ``crit_separation`` from ``star``.
Parameters
----------
star : `~astropy.table.Row`
Star which will be either the head of a cluster or an
isolated one.
starlist : `~astropy.table.Table`
List of star positions. Columns named as ``x_0`` and
``y_0``, which corresponds to the centroid coordinates of
the sources, must be provided.
Returns
-------
Array containing the ids of those stars which are at a distance less
than ``crit_separation`` from ``star``.
"""
star_distance = np.hypot(star['x_0'] - starlist['x_0'],
star['y_0'] - starlist['y_0'])
distance_criteria = star_distance < self.crit_separation
return np.asarray(starlist[distance_criteria]['id']) | python | def find_group(self, star, starlist):
"""
Find the ids of those stars in ``starlist`` which are at a
distance less than ``crit_separation`` from ``star``.
Parameters
----------
star : `~astropy.table.Row`
Star which will be either the head of a cluster or an
isolated one.
starlist : `~astropy.table.Table`
List of star positions. Columns named as ``x_0`` and
``y_0``, which corresponds to the centroid coordinates of
the sources, must be provided.
Returns
-------
Array containing the ids of those stars which are at a distance less
than ``crit_separation`` from ``star``.
"""
star_distance = np.hypot(star['x_0'] - starlist['x_0'],
star['y_0'] - starlist['y_0'])
distance_criteria = star_distance < self.crit_separation
return np.asarray(starlist[distance_criteria]['id']) | [
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starlist : `~astropy.table.Table`
List of star positions. Columns named as ``x_0`` and
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astropy/photutils | photutils/aperture/bounding_box.py | BoundingBox._from_float | def _from_float(cls, xmin, xmax, ymin, ymax):
"""
Return the smallest bounding box that fully contains a given
rectangle defined by float coordinate values.
Following the pixel index convention, an integer index
corresponds to the center of a pixel and the pixel edges span
from (index - 0.5) to (index + 0.5). For example, the pixel
edge spans of the following pixels are:
- pixel 0: from -0.5 to 0.5
- pixel 1: from 0.5 to 1.5
- pixel 2: from 1.5 to 2.5
In addition, because `BoundingBox` upper limits are exclusive
(by definition), 1 is added to the upper pixel edges. See
examples below.
Parameters
----------
xmin, xmax, ymin, ymax : float
Float coordinates defining a rectangle. The lower values
(``xmin`` and ``ymin``) must not be greater than the
respective upper values (``xmax`` and ``ymax``).
Returns
-------
bbox : `BoundingBox` object
The minimal ``BoundingBox`` object fully containing the
input rectangle coordinates.
Examples
--------
>>> from photutils import BoundingBox
>>> BoundingBox._from_float(xmin=1.0, xmax=10.0, ymin=2.0, ymax=20.0)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=21)
>>> BoundingBox._from_float(xmin=1.4, xmax=10.4, ymin=1.6, ymax=10.6)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=12)
"""
ixmin = int(np.floor(xmin + 0.5))
ixmax = int(np.ceil(xmax + 0.5))
iymin = int(np.floor(ymin + 0.5))
iymax = int(np.ceil(ymax + 0.5))
return cls(ixmin, ixmax, iymin, iymax) | python | def _from_float(cls, xmin, xmax, ymin, ymax):
"""
Return the smallest bounding box that fully contains a given
rectangle defined by float coordinate values.
Following the pixel index convention, an integer index
corresponds to the center of a pixel and the pixel edges span
from (index - 0.5) to (index + 0.5). For example, the pixel
edge spans of the following pixels are:
- pixel 0: from -0.5 to 0.5
- pixel 1: from 0.5 to 1.5
- pixel 2: from 1.5 to 2.5
In addition, because `BoundingBox` upper limits are exclusive
(by definition), 1 is added to the upper pixel edges. See
examples below.
Parameters
----------
xmin, xmax, ymin, ymax : float
Float coordinates defining a rectangle. The lower values
(``xmin`` and ``ymin``) must not be greater than the
respective upper values (``xmax`` and ``ymax``).
Returns
-------
bbox : `BoundingBox` object
The minimal ``BoundingBox`` object fully containing the
input rectangle coordinates.
Examples
--------
>>> from photutils import BoundingBox
>>> BoundingBox._from_float(xmin=1.0, xmax=10.0, ymin=2.0, ymax=20.0)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=21)
>>> BoundingBox._from_float(xmin=1.4, xmax=10.4, ymin=1.6, ymax=10.6)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=12)
"""
ixmin = int(np.floor(xmin + 0.5))
ixmax = int(np.ceil(xmax + 0.5))
iymin = int(np.floor(ymin + 0.5))
iymax = int(np.ceil(ymax + 0.5))
return cls(ixmin, ixmax, iymin, iymax) | [
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Float coordinates defining a rectangle. The lower values
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Returns
-------
bbox : `BoundingBox` object
The minimal ``BoundingBox`` object fully containing the
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Examples
--------
>>> from photutils import BoundingBox
>>> BoundingBox._from_float(xmin=1.0, xmax=10.0, ymin=2.0, ymax=20.0)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=21)
>>> BoundingBox._from_float(xmin=1.4, xmax=10.4, ymin=1.6, ymax=10.6)
BoundingBox(ixmin=1, ixmax=11, iymin=2, iymax=12) | [
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astropy/photutils | photutils/aperture/bounding_box.py | BoundingBox.slices | def slices(self):
"""
The bounding box as a tuple of `slice` objects.
The slice tuple is in numpy axis order (i.e. ``(y, x)``) and
therefore can be used to slice numpy arrays.
"""
return (slice(self.iymin, self.iymax), slice(self.ixmin, self.ixmax)) | python | def slices(self):
"""
The bounding box as a tuple of `slice` objects.
The slice tuple is in numpy axis order (i.e. ``(y, x)``) and
therefore can be used to slice numpy arrays.
"""
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astropy/photutils | photutils/aperture/bounding_box.py | BoundingBox.as_patch | def as_patch(self, **kwargs):
"""
Return a `matplotlib.patches.Rectangle` that represents the
bounding box.
Parameters
----------
kwargs
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
Returns
-------
result : `matplotlib.patches.Rectangle`
A matplotlib rectangular patch.
Examples
--------
.. plot::
:include-source:
import matplotlib.pyplot as plt
from photutils import BoundingBox
bbox = BoundingBox(2, 7, 3, 8)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
np.random.seed(12345)
ax.imshow(np.random.random((10, 10)), interpolation='nearest',
cmap='viridis')
ax.add_patch(bbox.as_patch(facecolor='none', edgecolor='white',
lw=2.))
"""
from matplotlib.patches import Rectangle
return Rectangle(xy=(self.extent[0], self.extent[2]),
width=self.shape[1], height=self.shape[0], **kwargs) | python | def as_patch(self, **kwargs):
"""
Return a `matplotlib.patches.Rectangle` that represents the
bounding box.
Parameters
----------
kwargs
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
Returns
-------
result : `matplotlib.patches.Rectangle`
A matplotlib rectangular patch.
Examples
--------
.. plot::
:include-source:
import matplotlib.pyplot as plt
from photutils import BoundingBox
bbox = BoundingBox(2, 7, 3, 8)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
np.random.seed(12345)
ax.imshow(np.random.random((10, 10)), interpolation='nearest',
cmap='viridis')
ax.add_patch(bbox.as_patch(facecolor='none', edgecolor='white',
lw=2.))
"""
from matplotlib.patches import Rectangle
return Rectangle(xy=(self.extent[0], self.extent[2]),
width=self.shape[1], height=self.shape[0], **kwargs) | [
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.. plot::
:include-source:
import matplotlib.pyplot as plt
from photutils import BoundingBox
bbox = BoundingBox(2, 7, 3, 8)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
np.random.seed(12345)
ax.imshow(np.random.random((10, 10)), interpolation='nearest',
cmap='viridis')
ax.add_patch(bbox.as_patch(facecolor='none', edgecolor='white',
lw=2.)) | [
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astropy/photutils | photutils/aperture/bounding_box.py | BoundingBox.to_aperture | def to_aperture(self):
"""
Return a `~photutils.aperture.RectangularAperture` that
represents the bounding box.
"""
from .rectangle import RectangularAperture
xpos = (self.extent[1] + self.extent[0]) / 2.
ypos = (self.extent[3] + self.extent[2]) / 2.
xypos = (xpos, ypos)
h, w = self.shape
return RectangularAperture(xypos, w=w, h=h, theta=0.) | python | def to_aperture(self):
"""
Return a `~photutils.aperture.RectangularAperture` that
represents the bounding box.
"""
from .rectangle import RectangularAperture
xpos = (self.extent[1] + self.extent[0]) / 2.
ypos = (self.extent[3] + self.extent[2]) / 2.
xypos = (xpos, ypos)
h, w = self.shape
return RectangularAperture(xypos, w=w, h=h, theta=0.) | [
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astropy/photutils | photutils/aperture/bounding_box.py | BoundingBox.plot | def plot(self, origin=(0, 0), ax=None, fill=False, **kwargs):
"""
Plot the `BoundingBox` on a matplotlib `~matplotlib.axes.Axes`
instance.
Parameters
----------
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
ax : `matplotlib.axes.Axes` instance, optional
If `None`, then the current `~matplotlib.axes.Axes` instance
is used.
fill : bool, optional
Set whether to fill the aperture patch. The default is
`False`.
kwargs
Any keyword arguments accepted by `matplotlib.patches.Patch`.
"""
aper = self.to_aperture()
aper.plot(origin=origin, ax=ax, fill=fill, **kwargs) | python | def plot(self, origin=(0, 0), ax=None, fill=False, **kwargs):
"""
Plot the `BoundingBox` on a matplotlib `~matplotlib.axes.Axes`
instance.
Parameters
----------
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
ax : `matplotlib.axes.Axes` instance, optional
If `None`, then the current `~matplotlib.axes.Axes` instance
is used.
fill : bool, optional
Set whether to fill the aperture patch. The default is
`False`.
kwargs
Any keyword arguments accepted by `matplotlib.patches.Patch`.
"""
aper = self.to_aperture()
aper.plot(origin=origin, ax=ax, fill=fill, **kwargs) | [
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Set whether to fill the aperture patch. The default is
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kwargs
Any keyword arguments accepted by `matplotlib.patches.Patch`. | [
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astropy/photutils | photutils/detection/findstars.py | _find_stars | def _find_stars(data, kernel, threshold_eff, min_separation=None,
mask=None, exclude_border=False):
"""
Find stars in an image.
Parameters
----------
data : 2D array_like
The 2D array of the image.
kernel : `_StarFinderKernel`
The convolution kernel.
threshold_eff : float
The absolute image value above which to select sources. This
threshold should be the threshold input to the star finder class
multiplied by the kernel relerr.
mask : 2D bool array, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when searching for stars.
exclude_border : bool, optional
Set to `True` to exclude sources found within half the size of
the convolution kernel from the image borders. The default is
`False`, which is the mode used by IRAF's `DAOFIND`_ and
`starfind`_ tasks.
Returns
-------
objects : list of `_StarCutout`
A list of `_StarCutout` objects containing the image cutout for
each source.
.. _DAOFIND: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?daofind
.. _starfind: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?starfind
"""
convolved_data = filter_data(data, kernel.data, mode='constant',
fill_value=0.0, check_normalization=False)
# define a local footprint for the peak finder
if min_separation is None: # daofind
footprint = kernel.mask.astype(np.bool)
else:
# define a circular footprint
idx = np.arange(-min_separation, min_separation + 1)
xx, yy = np.meshgrid(idx, idx)
footprint = np.array((xx**2 + yy**2) <= min_separation**2, dtype=int)
# pad the data and convolved image by the kernel x/y radius to allow
# for detections near the edges
if not exclude_border:
ypad = kernel.yradius
xpad = kernel.xradius
pad = ((ypad, ypad), (xpad, xpad))
# mode must be a string for numpy < 0.11
# (see https://github.com/numpy/numpy/issues/7112)
mode = str('constant')
data = np.pad(data, pad, mode=mode, constant_values=[0.])
if mask is not None:
mask = np.pad(mask, pad, mode=mode, constant_values=[0.])
convolved_data = np.pad(convolved_data, pad, mode=mode,
constant_values=[0.])
# find local peaks in the convolved data
with warnings.catch_warnings():
# suppress any NoDetectionsWarning from find_peaks
warnings.filterwarnings('ignore', category=NoDetectionsWarning)
tbl = find_peaks(convolved_data, threshold_eff, footprint=footprint,
mask=mask)
if tbl is None:
return None
coords = np.transpose([tbl['y_peak'], tbl['x_peak']])
star_cutouts = []
for (ypeak, xpeak) in coords:
# now extract the object from the data, centered on the peak
# pixel in the convolved image, with the same size as the kernel
x0 = xpeak - kernel.xradius
x1 = xpeak + kernel.xradius + 1
y0 = ypeak - kernel.yradius
y1 = ypeak + kernel.yradius + 1
if x0 < 0 or x1 > data.shape[1]:
continue # pragma: no cover
if y0 < 0 or y1 > data.shape[0]:
continue # pragma: no cover
slices = (slice(y0, y1), slice(x0, x1))
data_cutout = data[slices]
convdata_cutout = convolved_data[slices]
# correct pixel values for the previous image padding
if not exclude_border:
x0 -= kernel.xradius
x1 -= kernel.xradius
y0 -= kernel.yradius
y1 -= kernel.yradius
xpeak -= kernel.xradius
ypeak -= kernel.yradius
slices = (slice(y0, y1), slice(x0, x1))
star_cutouts.append(_StarCutout(data_cutout, convdata_cutout, slices,
xpeak, ypeak, kernel, threshold_eff))
return star_cutouts | python | def _find_stars(data, kernel, threshold_eff, min_separation=None,
mask=None, exclude_border=False):
"""
Find stars in an image.
Parameters
----------
data : 2D array_like
The 2D array of the image.
kernel : `_StarFinderKernel`
The convolution kernel.
threshold_eff : float
The absolute image value above which to select sources. This
threshold should be the threshold input to the star finder class
multiplied by the kernel relerr.
mask : 2D bool array, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when searching for stars.
exclude_border : bool, optional
Set to `True` to exclude sources found within half the size of
the convolution kernel from the image borders. The default is
`False`, which is the mode used by IRAF's `DAOFIND`_ and
`starfind`_ tasks.
Returns
-------
objects : list of `_StarCutout`
A list of `_StarCutout` objects containing the image cutout for
each source.
.. _DAOFIND: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?daofind
.. _starfind: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?starfind
"""
convolved_data = filter_data(data, kernel.data, mode='constant',
fill_value=0.0, check_normalization=False)
# define a local footprint for the peak finder
if min_separation is None: # daofind
footprint = kernel.mask.astype(np.bool)
else:
# define a circular footprint
idx = np.arange(-min_separation, min_separation + 1)
xx, yy = np.meshgrid(idx, idx)
footprint = np.array((xx**2 + yy**2) <= min_separation**2, dtype=int)
# pad the data and convolved image by the kernel x/y radius to allow
# for detections near the edges
if not exclude_border:
ypad = kernel.yradius
xpad = kernel.xradius
pad = ((ypad, ypad), (xpad, xpad))
# mode must be a string for numpy < 0.11
# (see https://github.com/numpy/numpy/issues/7112)
mode = str('constant')
data = np.pad(data, pad, mode=mode, constant_values=[0.])
if mask is not None:
mask = np.pad(mask, pad, mode=mode, constant_values=[0.])
convolved_data = np.pad(convolved_data, pad, mode=mode,
constant_values=[0.])
# find local peaks in the convolved data
with warnings.catch_warnings():
# suppress any NoDetectionsWarning from find_peaks
warnings.filterwarnings('ignore', category=NoDetectionsWarning)
tbl = find_peaks(convolved_data, threshold_eff, footprint=footprint,
mask=mask)
if tbl is None:
return None
coords = np.transpose([tbl['y_peak'], tbl['x_peak']])
star_cutouts = []
for (ypeak, xpeak) in coords:
# now extract the object from the data, centered on the peak
# pixel in the convolved image, with the same size as the kernel
x0 = xpeak - kernel.xradius
x1 = xpeak + kernel.xradius + 1
y0 = ypeak - kernel.yradius
y1 = ypeak + kernel.yradius + 1
if x0 < 0 or x1 > data.shape[1]:
continue # pragma: no cover
if y0 < 0 or y1 > data.shape[0]:
continue # pragma: no cover
slices = (slice(y0, y1), slice(x0, x1))
data_cutout = data[slices]
convdata_cutout = convolved_data[slices]
# correct pixel values for the previous image padding
if not exclude_border:
x0 -= kernel.xradius
x1 -= kernel.xradius
y0 -= kernel.yradius
y1 -= kernel.yradius
xpeak -= kernel.xradius
ypeak -= kernel.yradius
slices = (slice(y0, y1), slice(x0, x1))
star_cutouts.append(_StarCutout(data_cutout, convdata_cutout, slices,
xpeak, ypeak, kernel, threshold_eff))
return star_cutouts | [
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Parameters
----------
data : 2D array_like
The 2D array of the image.
kernel : `_StarFinderKernel`
The convolution kernel.
threshold_eff : float
The absolute image value above which to select sources. This
threshold should be the threshold input to the star finder class
multiplied by the kernel relerr.
mask : 2D bool array, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when searching for stars.
exclude_border : bool, optional
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the convolution kernel from the image borders. The default is
`False`, which is the mode used by IRAF's `DAOFIND`_ and
`starfind`_ tasks.
Returns
-------
objects : list of `_StarCutout`
A list of `_StarCutout` objects containing the image cutout for
each source.
.. _DAOFIND: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?daofind
.. _starfind: http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?starfind | [
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astropy/photutils | photutils/detection/findstars.py | _DAOFind_Properties.roundness2 | def roundness2(self):
"""
The star roundness.
This roundness parameter represents the ratio of the difference
in the height of the best fitting Gaussian function in x minus
the best fitting Gaussian function in y, divided by the average
of the best fitting Gaussian functions in x and y. A circular
source will have a zero roundness. A source extended in x or y
will have a negative or positive roundness, respectively.
"""
if np.isnan(self.hx) or np.isnan(self.hy):
return np.nan
else:
return 2.0 * (self.hx - self.hy) / (self.hx + self.hy) | python | def roundness2(self):
"""
The star roundness.
This roundness parameter represents the ratio of the difference
in the height of the best fitting Gaussian function in x minus
the best fitting Gaussian function in y, divided by the average
of the best fitting Gaussian functions in x and y. A circular
source will have a zero roundness. A source extended in x or y
will have a negative or positive roundness, respectively.
"""
if np.isnan(self.hx) or np.isnan(self.hy):
return np.nan
else:
return 2.0 * (self.hx - self.hy) / (self.hx + self.hy) | [
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the best fitting Gaussian function in y, divided by the average
of the best fitting Gaussian functions in x and y. A circular
source will have a zero roundness. A source extended in x or y
will have a negative or positive roundness, respectively. | [
"The",
"star",
"roundness",
"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/detection/findstars.py#L426-L441 | train |
astropy/photutils | photutils/segmentation/detect.py | detect_sources | def detect_sources(data, threshold, npixels, filter_kernel=None,
connectivity=8, mask=None):
"""
Detect sources above a specified threshold value in an image and
return a `~photutils.segmentation.SegmentationImage` object.
Detected sources must have ``npixels`` connected pixels that are
each greater than the ``threshold`` value. If the filtering option
is used, then the ``threshold`` is applied to the filtered image.
The input ``mask`` can be used to mask pixels in the input data.
Masked pixels will not be included in any source.
This function does not deblend overlapping sources. First use this
function to detect sources followed by
:func:`~photutils.segmentation.deblend_sources` to deblend sources.
Parameters
----------
data : array_like
The 2D array of the image.
threshold : float or array-like
The data value or pixel-wise data values to be used for the
detection threshold. A 2D ``threshold`` must have the same
shape as ``data``. See `~photutils.detection.detect_threshold`
for one way to create a ``threshold`` image.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
connectivity : {4, 8}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 4 or 8
(default). 4-connected pixels touch along their edges.
8-connected pixels touch along their edges or corners. For
reference, SExtractor uses 8-connected pixels.
mask : array_like (bool)
A boolean mask, with the same shape as the input ``data``, where
`True` values indicate masked pixels. Masked pixels will not be
included in any source.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage` or `None`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background. If no sources
are found then `None` is returned.
See Also
--------
:func:`photutils.detection.detect_threshold`,
:class:`photutils.segmentation.SegmentationImage`,
:func:`photutils.segmentation.source_properties`
:func:`photutils.segmentation.deblend_sources`
Examples
--------
.. plot::
:include-source:
# make a table of Gaussian sources
from astropy.table import Table
table = Table()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.array([145., 20., 0., 60.]) * np.pi / 180.
# make an image of the sources with Gaussian noise
from photutils.datasets import make_gaussian_sources_image
from photutils.datasets import make_noise_image
shape = (100, 200)
sources = make_gaussian_sources_image(shape, table)
noise = make_noise_image(shape, type='gaussian', mean=0.,
stddev=5., random_state=12345)
image = sources + noise
# detect the sources
from photutils import detect_threshold, detect_sources
threshold = detect_threshold(image, snr=3)
from astropy.convolution import Gaussian2DKernel
sigma = 3.0 / (2.0 * np.sqrt(2.0 * np.log(2.0))) # FWHM = 3
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(image, threshold, npixels=5,
filter_kernel=kernel)
# plot the image and the segmentation image
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(image, origin='lower', interpolation='nearest')
ax2.imshow(segm.data, origin='lower', interpolation='nearest')
"""
from scipy import ndimage
if (npixels <= 0) or (int(npixels) != npixels):
raise ValueError('npixels must be a positive integer, got '
'"{0}"'.format(npixels))
image = (filter_data(data, filter_kernel, mode='constant', fill_value=0.0,
check_normalization=True) > threshold)
if mask is not None:
if mask.shape != image.shape:
raise ValueError('mask must have the same shape as the input '
'image.')
image &= ~mask
if connectivity == 4:
selem = ndimage.generate_binary_structure(2, 1)
elif connectivity == 8:
selem = ndimage.generate_binary_structure(2, 2)
else:
raise ValueError('Invalid connectivity={0}. '
'Options are 4 or 8'.format(connectivity))
segm_img, nobj = ndimage.label(image, structure=selem)
# remove objects with less than npixels
# NOTE: for typical data, making the cutout images is ~10x faster
# than using segm_img directly
segm_slices = ndimage.find_objects(segm_img)
for i, slices in enumerate(segm_slices):
cutout = segm_img[slices]
segment_mask = (cutout == (i+1))
if np.count_nonzero(segment_mask) < npixels:
cutout[segment_mask] = 0
# now relabel to make consecutive label indices
segm_img, nobj = ndimage.label(segm_img, structure=selem)
if nobj == 0:
warnings.warn('No sources were found.', NoDetectionsWarning)
return None
else:
return SegmentationImage(segm_img) | python | def detect_sources(data, threshold, npixels, filter_kernel=None,
connectivity=8, mask=None):
"""
Detect sources above a specified threshold value in an image and
return a `~photutils.segmentation.SegmentationImage` object.
Detected sources must have ``npixels`` connected pixels that are
each greater than the ``threshold`` value. If the filtering option
is used, then the ``threshold`` is applied to the filtered image.
The input ``mask`` can be used to mask pixels in the input data.
Masked pixels will not be included in any source.
This function does not deblend overlapping sources. First use this
function to detect sources followed by
:func:`~photutils.segmentation.deblend_sources` to deblend sources.
Parameters
----------
data : array_like
The 2D array of the image.
threshold : float or array-like
The data value or pixel-wise data values to be used for the
detection threshold. A 2D ``threshold`` must have the same
shape as ``data``. See `~photutils.detection.detect_threshold`
for one way to create a ``threshold`` image.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
connectivity : {4, 8}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 4 or 8
(default). 4-connected pixels touch along their edges.
8-connected pixels touch along their edges or corners. For
reference, SExtractor uses 8-connected pixels.
mask : array_like (bool)
A boolean mask, with the same shape as the input ``data``, where
`True` values indicate masked pixels. Masked pixels will not be
included in any source.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage` or `None`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background. If no sources
are found then `None` is returned.
See Also
--------
:func:`photutils.detection.detect_threshold`,
:class:`photutils.segmentation.SegmentationImage`,
:func:`photutils.segmentation.source_properties`
:func:`photutils.segmentation.deblend_sources`
Examples
--------
.. plot::
:include-source:
# make a table of Gaussian sources
from astropy.table import Table
table = Table()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.array([145., 20., 0., 60.]) * np.pi / 180.
# make an image of the sources with Gaussian noise
from photutils.datasets import make_gaussian_sources_image
from photutils.datasets import make_noise_image
shape = (100, 200)
sources = make_gaussian_sources_image(shape, table)
noise = make_noise_image(shape, type='gaussian', mean=0.,
stddev=5., random_state=12345)
image = sources + noise
# detect the sources
from photutils import detect_threshold, detect_sources
threshold = detect_threshold(image, snr=3)
from astropy.convolution import Gaussian2DKernel
sigma = 3.0 / (2.0 * np.sqrt(2.0 * np.log(2.0))) # FWHM = 3
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(image, threshold, npixels=5,
filter_kernel=kernel)
# plot the image and the segmentation image
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(image, origin='lower', interpolation='nearest')
ax2.imshow(segm.data, origin='lower', interpolation='nearest')
"""
from scipy import ndimage
if (npixels <= 0) or (int(npixels) != npixels):
raise ValueError('npixels must be a positive integer, got '
'"{0}"'.format(npixels))
image = (filter_data(data, filter_kernel, mode='constant', fill_value=0.0,
check_normalization=True) > threshold)
if mask is not None:
if mask.shape != image.shape:
raise ValueError('mask must have the same shape as the input '
'image.')
image &= ~mask
if connectivity == 4:
selem = ndimage.generate_binary_structure(2, 1)
elif connectivity == 8:
selem = ndimage.generate_binary_structure(2, 2)
else:
raise ValueError('Invalid connectivity={0}. '
'Options are 4 or 8'.format(connectivity))
segm_img, nobj = ndimage.label(image, structure=selem)
# remove objects with less than npixels
# NOTE: for typical data, making the cutout images is ~10x faster
# than using segm_img directly
segm_slices = ndimage.find_objects(segm_img)
for i, slices in enumerate(segm_slices):
cutout = segm_img[slices]
segment_mask = (cutout == (i+1))
if np.count_nonzero(segment_mask) < npixels:
cutout[segment_mask] = 0
# now relabel to make consecutive label indices
segm_img, nobj = ndimage.label(segm_img, structure=selem)
if nobj == 0:
warnings.warn('No sources were found.', NoDetectionsWarning)
return None
else:
return SegmentationImage(segm_img) | [
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return a `~photutils.segmentation.SegmentationImage` object.
Detected sources must have ``npixels`` connected pixels that are
each greater than the ``threshold`` value. If the filtering option
is used, then the ``threshold`` is applied to the filtered image.
The input ``mask`` can be used to mask pixels in the input data.
Masked pixels will not be included in any source.
This function does not deblend overlapping sources. First use this
function to detect sources followed by
:func:`~photutils.segmentation.deblend_sources` to deblend sources.
Parameters
----------
data : array_like
The 2D array of the image.
threshold : float or array-like
The data value or pixel-wise data values to be used for the
detection threshold. A 2D ``threshold`` must have the same
shape as ``data``. See `~photutils.detection.detect_threshold`
for one way to create a ``threshold`` image.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
connectivity : {4, 8}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 4 or 8
(default). 4-connected pixels touch along their edges.
8-connected pixels touch along their edges or corners. For
reference, SExtractor uses 8-connected pixels.
mask : array_like (bool)
A boolean mask, with the same shape as the input ``data``, where
`True` values indicate masked pixels. Masked pixels will not be
included in any source.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage` or `None`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background. If no sources
are found then `None` is returned.
See Also
--------
:func:`photutils.detection.detect_threshold`,
:class:`photutils.segmentation.SegmentationImage`,
:func:`photutils.segmentation.source_properties`
:func:`photutils.segmentation.deblend_sources`
Examples
--------
.. plot::
:include-source:
# make a table of Gaussian sources
from astropy.table import Table
table = Table()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.array([145., 20., 0., 60.]) * np.pi / 180.
# make an image of the sources with Gaussian noise
from photutils.datasets import make_gaussian_sources_image
from photutils.datasets import make_noise_image
shape = (100, 200)
sources = make_gaussian_sources_image(shape, table)
noise = make_noise_image(shape, type='gaussian', mean=0.,
stddev=5., random_state=12345)
image = sources + noise
# detect the sources
from photutils import detect_threshold, detect_sources
threshold = detect_threshold(image, snr=3)
from astropy.convolution import Gaussian2DKernel
sigma = 3.0 / (2.0 * np.sqrt(2.0 * np.log(2.0))) # FWHM = 3
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(image, threshold, npixels=5,
filter_kernel=kernel)
# plot the image and the segmentation image
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(image, origin='lower', interpolation='nearest')
ax2.imshow(segm.data, origin='lower', interpolation='nearest') | [
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astropy/photutils | photutils/segmentation/detect.py | make_source_mask | def make_source_mask(data, snr, npixels, mask=None, mask_value=None,
filter_fwhm=None, filter_size=3, filter_kernel=None,
sigclip_sigma=3.0, sigclip_iters=5, dilate_size=11):
"""
Make a source mask using source segmentation and binary dilation.
Parameters
----------
data : array_like
The 2D array of the image.
snr : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
filter_fwhm : float, optional
The full-width at half-maximum (FWHM) of the Gaussian kernel to
filter the image before thresholding. ``filter_fwhm`` and
``filter_size`` are ignored if ``filter_kernel`` is defined.
filter_size : float, optional
The size of the square Gaussian kernel image. Used only if
``filter_fwhm`` is defined. ``filter_fwhm`` and ``filter_size``
are ignored if ``filter_kernel`` is defined.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel. ``filter_kernel`` overrides ``filter_fwhm`` and
``filter_size``.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
when calculating the image background statistics.
sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
clip until convergence is achieved (i.e., continue until the last
iteration clips nothing) when calculating the image background
statistics.
dilate_size : int, optional
The size of the square array used to dilate the segmentation
image.
Returns
-------
mask : 2D `~numpy.ndarray`, bool
A 2D boolean image containing the source mask.
"""
from scipy import ndimage
threshold = detect_threshold(data, snr, background=None, error=None,
mask=mask, mask_value=None,
sigclip_sigma=sigclip_sigma,
sigclip_iters=sigclip_iters)
kernel = None
if filter_kernel is not None:
kernel = filter_kernel
if filter_fwhm is not None:
sigma = filter_fwhm * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=filter_size,
y_size=filter_size)
if kernel is not None:
kernel.normalize()
segm = detect_sources(data, threshold, npixels, filter_kernel=kernel)
selem = np.ones((dilate_size, dilate_size))
return ndimage.binary_dilation(segm.data.astype(np.bool), selem) | python | def make_source_mask(data, snr, npixels, mask=None, mask_value=None,
filter_fwhm=None, filter_size=3, filter_kernel=None,
sigclip_sigma=3.0, sigclip_iters=5, dilate_size=11):
"""
Make a source mask using source segmentation and binary dilation.
Parameters
----------
data : array_like
The 2D array of the image.
snr : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
filter_fwhm : float, optional
The full-width at half-maximum (FWHM) of the Gaussian kernel to
filter the image before thresholding. ``filter_fwhm`` and
``filter_size`` are ignored if ``filter_kernel`` is defined.
filter_size : float, optional
The size of the square Gaussian kernel image. Used only if
``filter_fwhm`` is defined. ``filter_fwhm`` and ``filter_size``
are ignored if ``filter_kernel`` is defined.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel. ``filter_kernel`` overrides ``filter_fwhm`` and
``filter_size``.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
when calculating the image background statistics.
sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
clip until convergence is achieved (i.e., continue until the last
iteration clips nothing) when calculating the image background
statistics.
dilate_size : int, optional
The size of the square array used to dilate the segmentation
image.
Returns
-------
mask : 2D `~numpy.ndarray`, bool
A 2D boolean image containing the source mask.
"""
from scipy import ndimage
threshold = detect_threshold(data, snr, background=None, error=None,
mask=mask, mask_value=None,
sigclip_sigma=sigclip_sigma,
sigclip_iters=sigclip_iters)
kernel = None
if filter_kernel is not None:
kernel = filter_kernel
if filter_fwhm is not None:
sigma = filter_fwhm * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=filter_size,
y_size=filter_size)
if kernel is not None:
kernel.normalize()
segm = detect_sources(data, threshold, npixels, filter_kernel=kernel)
selem = np.ones((dilate_size, dilate_size))
return ndimage.binary_dilation(segm.data.astype(np.bool), selem) | [
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Parameters
----------
data : array_like
The 2D array of the image.
snr : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
mask : array_like, bool, optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are ignored when computing the image background
statistics.
mask_value : float, optional
An image data value (e.g., ``0.0``) that is ignored when
computing the image background statistics. ``mask_value`` will
be ignored if ``mask`` is input.
filter_fwhm : float, optional
The full-width at half-maximum (FWHM) of the Gaussian kernel to
filter the image before thresholding. ``filter_fwhm`` and
``filter_size`` are ignored if ``filter_kernel`` is defined.
filter_size : float, optional
The size of the square Gaussian kernel image. Used only if
``filter_fwhm`` is defined. ``filter_fwhm`` and ``filter_size``
are ignored if ``filter_kernel`` is defined.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel. ``filter_kernel`` overrides ``filter_fwhm`` and
``filter_size``.
sigclip_sigma : float, optional
The number of standard deviations to use as the clipping limit
when calculating the image background statistics.
sigclip_iters : int, optional
The number of iterations to perform sigma clipping, or `None` to
clip until convergence is achieved (i.e., continue until the last
iteration clips nothing) when calculating the image background
statistics.
dilate_size : int, optional
The size of the square array used to dilate the segmentation
image.
Returns
-------
mask : 2D `~numpy.ndarray`, bool
A 2D boolean image containing the source mask. | [
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astropy/photutils | photutils/segmentation/core.py | Segment.data_ma | def data_ma(self):
"""
A 2D `~numpy.ma.MaskedArray` cutout image of the segment using
the minimal bounding box.
The mask is `True` for pixels outside of the source segment
(i.e. neighboring segments within the rectangular cutout image
are masked).
"""
mask = (self._segment_img[self.slices] != self.label)
return np.ma.masked_array(self._segment_img[self.slices], mask=mask) | python | def data_ma(self):
"""
A 2D `~numpy.ma.MaskedArray` cutout image of the segment using
the minimal bounding box.
The mask is `True` for pixels outside of the source segment
(i.e. neighboring segments within the rectangular cutout image
are masked).
"""
mask = (self._segment_img[self.slices] != self.label)
return np.ma.masked_array(self._segment_img[self.slices], mask=mask) | [
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage._reset_lazy_properties | def _reset_lazy_properties(self):
"""Reset all lazy properties."""
for key, value in self.__class__.__dict__.items():
if isinstance(value, lazyproperty):
self.__dict__.pop(key, None) | python | def _reset_lazy_properties(self):
"""Reset all lazy properties."""
for key, value in self.__class__.__dict__.items():
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.segments | def segments(self):
"""
A list of `Segment` objects.
The list starts with the *non-zero* label. The returned list
has a length equal to the number of labels and matches the order
of the ``labels`` attribute.
"""
segments = []
for label, slc in zip(self.labels, self.slices):
segments.append(Segment(self.data, label, slc,
self.get_area(label)))
return segments | python | def segments(self):
"""
A list of `Segment` objects.
The list starts with the *non-zero* label. The returned list
has a length equal to the number of labels and matches the order
of the ``labels`` attribute.
"""
segments = []
for label, slc in zip(self.labels, self.slices):
segments.append(Segment(self.data, label, slc,
self.get_area(label)))
return segments | [
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.get_index | def get_index(self, label):
"""
Find the index of the input ``label``.
Parameters
----------
labels : int
The label numbers to find.
Returns
-------
index : int
The array index.
Raises
------
ValueError
If ``label`` is invalid.
"""
self.check_labels(label)
return np.searchsorted(self.labels, label) | python | def get_index(self, label):
"""
Find the index of the input ``label``.
Parameters
----------
labels : int
The label numbers to find.
Returns
-------
index : int
The array index.
Raises
------
ValueError
If ``label`` is invalid.
"""
self.check_labels(label)
return np.searchsorted(self.labels, label) | [
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index : int
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.get_indices | def get_indices(self, labels):
"""
Find the indices of the input ``labels``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to find.
Returns
-------
indices : int `~numpy.ndarray`
An integer array of indices with the same shape as
``labels``. If ``labels`` is a scalar, then the returned
index will also be a scalar.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
self.check_labels(labels)
return np.searchsorted(self.labels, labels) | python | def get_indices(self, labels):
"""
Find the indices of the input ``labels``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to find.
Returns
-------
indices : int `~numpy.ndarray`
An integer array of indices with the same shape as
``labels``. If ``labels`` is a scalar, then the returned
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Raises
------
ValueError
If any input ``labels`` are invalid.
"""
self.check_labels(labels)
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.slices | def slices(self):
"""
A list of tuples, where each tuple contains two slices
representing the minimal box that contains the labeled region.
The list starts with the *non-zero* label. The returned list
has a length equal to the number of labels and matches the order
of the ``labels`` attribute.
"""
from scipy.ndimage import find_objects
return [slc for slc in find_objects(self._data) if slc is not None] | python | def slices(self):
"""
A list of tuples, where each tuple contains two slices
representing the minimal box that contains the labeled region.
The list starts with the *non-zero* label. The returned list
has a length equal to the number of labels and matches the order
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"""
from scipy.ndimage import find_objects
return [slc for slc in find_objects(self._data) if slc is not None] | [
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.missing_labels | def missing_labels(self):
"""
A 1D `~numpy.ndarray` of the sorted non-zero labels that are
missing in the consecutive sequence from zero to the maximum
label number.
"""
return np.array(sorted(set(range(0, self.max_label + 1)).
difference(np.insert(self.labels, 0, 0)))) | python | def missing_labels(self):
"""
A 1D `~numpy.ndarray` of the sorted non-zero labels that are
missing in the consecutive sequence from zero to the maximum
label number.
"""
return np.array(sorted(set(range(0, self.max_label + 1)).
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.reassign_label | def reassign_label(self, label, new_label, relabel=False):
"""
Reassign a label number to a new number.
If ``new_label`` is already present in the segmentation image,
then it will be combined with the input ``label`` number.
Parameters
----------
labels : int
The label number to reassign.
new_label : int
The newly assigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4, relabel=True)
>>> segm.data
array([[2, 2, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 1, 1, 0, 0],
[4, 0, 0, 0, 0, 3],
[4, 4, 0, 3, 3, 3],
[4, 4, 0, 0, 3, 3]])
"""
self.reassign_labels(label, new_label, relabel=relabel) | python | def reassign_label(self, label, new_label, relabel=False):
"""
Reassign a label number to a new number.
If ``new_label`` is already present in the segmentation image,
then it will be combined with the input ``label`` number.
Parameters
----------
labels : int
The label number to reassign.
new_label : int
The newly assigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4, relabel=True)
>>> segm.data
array([[2, 2, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 1, 1, 0, 0],
[4, 0, 0, 0, 0, 3],
[4, 4, 0, 3, 3, 3],
[4, 4, 0, 0, 3, 3]])
"""
self.reassign_labels(label, new_label, relabel=relabel) | [
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The label number to reassign.
new_label : int
The newly assigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
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Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_label(label=1, new_label=4, relabel=True)
>>> segm.data
array([[2, 2, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 1, 1, 0, 0],
[4, 0, 0, 0, 0, 3],
[4, 4, 0, 3, 3, 3],
[4, 4, 0, 0, 3, 3]]) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L611-L680 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.reassign_labels | def reassign_labels(self, labels, new_label, relabel=False):
"""
Reassign one or more label numbers.
Multiple input ``labels`` will all be reassigned to the same
``new_label`` number. If ``new_label`` is already present in
the segmentation image, then it will be combined with the input
``labels``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to reassign.
new_label : int
The reassigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[2, 0, 0, 0, 0, 5],
[2, 2, 0, 5, 5, 5],
[2, 2, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[4, 0, 0, 0, 0, 5],
[4, 4, 0, 5, 5, 5],
[4, 4, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[1, 0, 0, 0, 0, 4],
[1, 1, 0, 4, 4, 4],
[1, 1, 0, 0, 4, 4]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
if len(labels) == 0:
return
idx = np.zeros(self.max_label + 1, dtype=int)
idx[self.labels] = self.labels
idx[labels] = new_label
# calling the data setter resets all cached properties
self.data = idx[self.data]
if relabel:
self.relabel_consecutive() | python | def reassign_labels(self, labels, new_label, relabel=False):
"""
Reassign one or more label numbers.
Multiple input ``labels`` will all be reassigned to the same
``new_label`` number. If ``new_label`` is already present in
the segmentation image, then it will be combined with the input
``labels``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to reassign.
new_label : int
The reassigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[2, 0, 0, 0, 0, 5],
[2, 2, 0, 5, 5, 5],
[2, 2, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[4, 0, 0, 0, 0, 5],
[4, 4, 0, 5, 5, 5],
[4, 4, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[1, 0, 0, 0, 0, 4],
[1, 1, 0, 4, 4, 4],
[1, 1, 0, 0, 4, 4]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
if len(labels) == 0:
return
idx = np.zeros(self.max_label + 1, dtype=int)
idx[self.labels] = self.labels
idx[labels] = new_label
# calling the data setter resets all cached properties
self.data = idx[self.data]
if relabel:
self.relabel_consecutive() | [
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Multiple input ``labels`` will all be reassigned to the same
``new_label`` number. If ``new_label`` is already present in
the segmentation image, then it will be combined with the input
``labels``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to reassign.
new_label : int
The reassigned label number.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[2, 0, 0, 0, 0, 5],
[2, 2, 0, 5, 5, 5],
[2, 2, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[4, 0, 0, 0, 0, 5],
[4, 4, 0, 5, 5, 5],
[4, 4, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.reassign_labels(labels=[1, 7], new_label=2, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[1, 0, 0, 0, 0, 4],
[1, 1, 0, 4, 4, 4],
[1, 1, 0, 0, 4, 4]]) | [
"Reassign",
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"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L682-L767 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.relabel_consecutive | def relabel_consecutive(self, start_label=1):
"""
Reassign the label numbers consecutively, such that there are no
missing label numbers.
Parameters
----------
start_label : int, optional
The starting label number, which should be a positive
integer. The default is 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel_consecutive()
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[5, 0, 0, 0, 0, 4],
[5, 5, 0, 4, 4, 4],
[5, 5, 0, 0, 4, 4]])
"""
if start_label <= 0:
raise ValueError('start_label must be > 0.')
if self.is_consecutive and (self.labels[0] == start_label):
return
new_labels = np.zeros(self.max_label + 1, dtype=np.int)
new_labels[self.labels] = np.arange(self.nlabels) + start_label
self.data = new_labels[self.data] | python | def relabel_consecutive(self, start_label=1):
"""
Reassign the label numbers consecutively, such that there are no
missing label numbers.
Parameters
----------
start_label : int, optional
The starting label number, which should be a positive
integer. The default is 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel_consecutive()
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[5, 0, 0, 0, 0, 4],
[5, 5, 0, 4, 4, 4],
[5, 5, 0, 0, 4, 4]])
"""
if start_label <= 0:
raise ValueError('start_label must be > 0.')
if self.is_consecutive and (self.labels[0] == start_label):
return
new_labels = np.zeros(self.max_label + 1, dtype=np.int)
new_labels[self.labels] = np.arange(self.nlabels) + start_label
self.data = new_labels[self.data] | [
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] | Reassign the label numbers consecutively, such that there are no
missing label numbers.
Parameters
----------
start_label : int, optional
The starting label number, which should be a positive
integer. The default is 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel_consecutive()
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[5, 0, 0, 0, 0, 4],
[5, 5, 0, 4, 4, 4],
[5, 5, 0, 0, 4, 4]]) | [
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astropy/photutils | photutils/segmentation/core.py | SegmentationImage.keep_label | def keep_label(self, label, relabel=False):
"""
Keep only the specified label.
Parameters
----------
label : int
The label number to keep.
relabel : bool, optional
If `True`, then the single segment will be assigned a label
value of 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3, relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
"""
self.keep_labels(label, relabel=relabel) | python | def keep_label(self, label, relabel=False):
"""
Keep only the specified label.
Parameters
----------
label : int
The label number to keep.
relabel : bool, optional
If `True`, then the single segment will be assigned a label
value of 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3, relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
"""
self.keep_labels(label, relabel=relabel) | [
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Parameters
----------
label : int
The label number to keep.
relabel : bool, optional
If `True`, then the single segment will be assigned a label
value of 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_label(label=3, relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]]) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L809-L856 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.keep_labels | def keep_labels(self, labels, relabel=False):
"""
Keep only the specified labels.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to keep.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3])
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 5, 5, 5],
[0, 0, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 2, 2, 2],
[0, 0, 0, 0, 2, 2]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
labels_tmp = list(set(self.labels) - set(labels))
self.remove_labels(labels_tmp, relabel=relabel) | python | def keep_labels(self, labels, relabel=False):
"""
Keep only the specified labels.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to keep.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3])
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 5, 5, 5],
[0, 0, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 2, 2, 2],
[0, 0, 0, 0, 2, 2]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
labels_tmp = list(set(self.labels) - set(labels))
self.remove_labels(labels_tmp, relabel=relabel) | [
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Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to keep.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3])
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 5, 5, 5],
[0, 0, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 2, 2, 2],
[0, 0, 0, 0, 2, 2]]) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L858-L910 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.remove_label | def remove_label(self, label, relabel=False):
"""
Remove the label number.
The removed label is assigned a value of zero (i.e.,
background).
Parameters
----------
label : int
The label number to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5)
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[4, 0, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0]])
"""
self.remove_labels(label, relabel=relabel) | python | def remove_label(self, label, relabel=False):
"""
Remove the label number.
The removed label is assigned a value of zero (i.e.,
background).
Parameters
----------
label : int
The label number to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5)
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[4, 0, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0]])
"""
self.remove_labels(label, relabel=relabel) | [
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The removed label is assigned a value of zero (i.e.,
background).
Parameters
----------
label : int
The label number to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5)
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_label(label=5, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[4, 0, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0]]) | [
"Remove",
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"number",
"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L912-L963 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.remove_labels | def remove_labels(self, labels, relabel=False):
"""
Remove one or more labels.
Removed labels are assigned a value of zero (i.e., background).
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3])
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0]])
"""
self.check_labels(labels)
self.reassign_label(labels, new_label=0)
if relabel:
self.relabel_consecutive() | python | def remove_labels(self, labels, relabel=False):
"""
Remove one or more labels.
Removed labels are assigned a value of zero (i.e., background).
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3])
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0]])
"""
self.check_labels(labels)
self.reassign_label(labels, new_label=0)
if relabel:
self.relabel_consecutive() | [
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Removed labels are assigned a value of zero (i.e., background).
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to remove.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3])
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0]]) | [
"Remove",
"one",
"or",
"more",
"labels",
"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L965-L1019 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.remove_border_labels | def remove_border_labels(self, border_width, partial_overlap=True,
relabel=False):
"""
Remove labeled segments near the image border.
Labels within the defined border region will be removed.
Parameters
----------
border_width : int
The width of the border region in pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into the border region will be removed.
Segments that are completely within the border region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1,
... partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if border_width >= min(self.shape) / 2:
raise ValueError('border_width must be smaller than half the '
'image size in either dimension')
border = np.zeros(self.shape, dtype=np.bool)
border[:border_width, :] = True
border[-border_width:, :] = True
border[:, :border_width] = True
border[:, -border_width:] = True
self.remove_masked_labels(border, partial_overlap=partial_overlap,
relabel=relabel) | python | def remove_border_labels(self, border_width, partial_overlap=True,
relabel=False):
"""
Remove labeled segments near the image border.
Labels within the defined border region will be removed.
Parameters
----------
border_width : int
The width of the border region in pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into the border region will be removed.
Segments that are completely within the border region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1,
... partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if border_width >= min(self.shape) / 2:
raise ValueError('border_width must be smaller than half the '
'image size in either dimension')
border = np.zeros(self.shape, dtype=np.bool)
border[:border_width, :] = True
border[-border_width:, :] = True
border[:, :border_width] = True
border[:, -border_width:] = True
self.remove_masked_labels(border, partial_overlap=partial_overlap,
relabel=relabel) | [
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Labels within the defined border region will be removed.
Parameters
----------
border_width : int
The width of the border region in pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into the border region will be removed.
Segments that are completely within the border region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1,
... partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]]) | [
"Remove",
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"image",
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"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L1021-L1088 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.remove_masked_labels | def remove_masked_labels(self, mask, partial_overlap=True,
relabel=False):
"""
Remove labeled segments located within a masked region.
Parameters
----------
mask : array_like (bool)
A boolean mask, with the same shape as the segmentation
image, where `True` values indicate masked pixels.
partial_overlap : bool, optional
If this is set to `True` (default), a segment that partially
extends into a masked region will also be removed. Segments
that are completely within a masked region are always
removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> mask = np.zeros_like(segm.data, dtype=np.bool)
>>> mask[0, :] = True # mask the first row
>>> segm.remove_masked_labels(mask)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_masked_labels(mask, partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if mask.shape != self.shape:
raise ValueError('mask must have the same shape as the '
'segmentation image')
remove_labels = self._get_labels(self.data[mask])
if not partial_overlap:
interior_labels = self._get_labels(self.data[~mask])
remove_labels = list(set(remove_labels) - set(interior_labels))
self.remove_labels(remove_labels, relabel=relabel) | python | def remove_masked_labels(self, mask, partial_overlap=True,
relabel=False):
"""
Remove labeled segments located within a masked region.
Parameters
----------
mask : array_like (bool)
A boolean mask, with the same shape as the segmentation
image, where `True` values indicate masked pixels.
partial_overlap : bool, optional
If this is set to `True` (default), a segment that partially
extends into a masked region will also be removed. Segments
that are completely within a masked region are always
removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> mask = np.zeros_like(segm.data, dtype=np.bool)
>>> mask[0, :] = True # mask the first row
>>> segm.remove_masked_labels(mask)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_masked_labels(mask, partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if mask.shape != self.shape:
raise ValueError('mask must have the same shape as the '
'segmentation image')
remove_labels = self._get_labels(self.data[mask])
if not partial_overlap:
interior_labels = self._get_labels(self.data[~mask])
remove_labels = list(set(remove_labels) - set(interior_labels))
self.remove_labels(remove_labels, relabel=relabel) | [
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Parameters
----------
mask : array_like (bool)
A boolean mask, with the same shape as the segmentation
image, where `True` values indicate masked pixels.
partial_overlap : bool, optional
If this is set to `True` (default), a segment that partially
extends into a masked region will also be removed. Segments
that are completely within a masked region are always
removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> mask = np.zeros_like(segm.data, dtype=np.bool)
>>> mask[0, :] = True # mask the first row
>>> segm.remove_masked_labels(mask)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_masked_labels(mask, partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]]) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L1090-L1155 | train |
astropy/photutils | photutils/segmentation/core.py | SegmentationImage.outline_segments | def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmentation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
from scipy.ndimage import grey_erosion, grey_dilation
# mode='constant' ensures outline is included on the image borders
selem = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
eroded = grey_erosion(self.data, footprint=selem, mode='constant',
cval=0.)
dilated = grey_dilation(self.data, footprint=selem, mode='constant',
cval=0.)
outlines = ((dilated != eroded) & (self.data != 0)).astype(int)
outlines *= self.data
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines | python | def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmentation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
from scipy.ndimage import grey_erosion, grey_dilation
# mode='constant' ensures outline is included on the image borders
selem = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
eroded = grey_erosion(self.data, footprint=selem, mode='constant',
cval=0.)
dilated = grey_dilation(self.data, footprint=selem, mode='constant',
cval=0.)
outlines = ((dilated != eroded) & (self.data != 0)).astype(int)
outlines *= self.data
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines | [
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The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmentation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]]) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/segmentation/core.py#L1157-L1213 | train |
astropy/photutils | photutils/aperture/mask.py | ApertureMask._overlap_slices | def _overlap_slices(self, shape):
"""
Calculate the slices for the overlapping part of the bounding
box and an array of the given shape.
Parameters
----------
shape : tuple of int
The ``(ny, nx)`` shape of array where the slices are to be
applied.
Returns
-------
slices_large : tuple of slices
A tuple of slice objects for each axis of the large array,
such that ``large_array[slices_large]`` extracts the region
of the large array that overlaps with the small array.
slices_small : slice
A tuple of slice objects for each axis of the small array,
such that ``small_array[slices_small]`` extracts the region
of the small array that is inside the large array.
"""
if len(shape) != 2:
raise ValueError('input shape must have 2 elements.')
xmin = self.bbox.ixmin
xmax = self.bbox.ixmax
ymin = self.bbox.iymin
ymax = self.bbox.iymax
if xmin >= shape[1] or ymin >= shape[0] or xmax <= 0 or ymax <= 0:
# no overlap of the aperture with the data
return None, None
slices_large = (slice(max(ymin, 0), min(ymax, shape[0])),
slice(max(xmin, 0), min(xmax, shape[1])))
slices_small = (slice(max(-ymin, 0),
min(ymax - ymin, shape[0] - ymin)),
slice(max(-xmin, 0),
min(xmax - xmin, shape[1] - xmin)))
return slices_large, slices_small | python | def _overlap_slices(self, shape):
"""
Calculate the slices for the overlapping part of the bounding
box and an array of the given shape.
Parameters
----------
shape : tuple of int
The ``(ny, nx)`` shape of array where the slices are to be
applied.
Returns
-------
slices_large : tuple of slices
A tuple of slice objects for each axis of the large array,
such that ``large_array[slices_large]`` extracts the region
of the large array that overlaps with the small array.
slices_small : slice
A tuple of slice objects for each axis of the small array,
such that ``small_array[slices_small]`` extracts the region
of the small array that is inside the large array.
"""
if len(shape) != 2:
raise ValueError('input shape must have 2 elements.')
xmin = self.bbox.ixmin
xmax = self.bbox.ixmax
ymin = self.bbox.iymin
ymax = self.bbox.iymax
if xmin >= shape[1] or ymin >= shape[0] or xmax <= 0 or ymax <= 0:
# no overlap of the aperture with the data
return None, None
slices_large = (slice(max(ymin, 0), min(ymax, shape[0])),
slice(max(xmin, 0), min(xmax, shape[1])))
slices_small = (slice(max(-ymin, 0),
min(ymax - ymin, shape[0] - ymin)),
slice(max(-xmin, 0),
min(xmax - xmin, shape[1] - xmin)))
return slices_large, slices_small | [
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shape : tuple of int
The ``(ny, nx)`` shape of array where the slices are to be
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Returns
-------
slices_large : tuple of slices
A tuple of slice objects for each axis of the large array,
such that ``large_array[slices_large]`` extracts the region
of the large array that overlaps with the small array.
slices_small : slice
A tuple of slice objects for each axis of the small array,
such that ``small_array[slices_small]`` extracts the region
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astropy/photutils | photutils/aperture/mask.py | ApertureMask.to_image | def to_image(self, shape):
"""
Return an image of the mask in a 2D array of the given shape,
taking any edge effects into account.
Parameters
----------
shape : tuple of int
The ``(ny, nx)`` shape of the output array.
Returns
-------
result : `~numpy.ndarray`
A 2D array of the mask.
"""
if len(shape) != 2:
raise ValueError('input shape must have 2 elements.')
image = np.zeros(shape)
if self.bbox.ixmin < 0 or self.bbox.iymin < 0:
return self._to_image_partial_overlap(image)
try:
image[self.bbox.slices] = self.data
except ValueError: # partial or no overlap
image = self._to_image_partial_overlap(image)
return image | python | def to_image(self, shape):
"""
Return an image of the mask in a 2D array of the given shape,
taking any edge effects into account.
Parameters
----------
shape : tuple of int
The ``(ny, nx)`` shape of the output array.
Returns
-------
result : `~numpy.ndarray`
A 2D array of the mask.
"""
if len(shape) != 2:
raise ValueError('input shape must have 2 elements.')
image = np.zeros(shape)
if self.bbox.ixmin < 0 or self.bbox.iymin < 0:
return self._to_image_partial_overlap(image)
try:
image[self.bbox.slices] = self.data
except ValueError: # partial or no overlap
image = self._to_image_partial_overlap(image)
return image | [
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astropy/photutils | photutils/aperture/mask.py | ApertureMask.cutout | def cutout(self, data, fill_value=0., copy=False):
"""
Create a cutout from the input data over the mask bounding box,
taking any edge effects into account.
Parameters
----------
data : array_like
A 2D array on which to apply the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
does not overlap with the input ``data``. The default is 0.
copy : bool, optional
If `True` then the returned cutout array will always be hold
a copy of the input ``data``. If `False` and the mask is
fully within the input ``data``, then the returned cutout
array will be a view into the input ``data``. In cases
where the mask partially overlaps or has no overlap with the
input ``data``, the returned cutout array will always hold a
copy of the input ``data`` (i.e. this keyword has no
effect).
Returns
-------
result : `~numpy.ndarray`
A 2D array cut out from the input ``data`` representing the
same cutout region as the aperture mask. If there is a
partial overlap of the aperture mask with the input data,
pixels outside of the data will be assigned to
``fill_value``. `None` is returned if there is no overlap
of the aperture with the input ``data``.
"""
data = np.asanyarray(data)
if data.ndim != 2:
raise ValueError('data must be a 2D array.')
partial_overlap = False
if self.bbox.ixmin < 0 or self.bbox.iymin < 0:
partial_overlap = True
if not partial_overlap:
# try this for speed -- the result may still be a partial
# overlap, in which case the next block will be triggered
if copy:
cutout = np.copy(data[self.bbox.slices])
else:
cutout = data[self.bbox.slices]
if partial_overlap or (cutout.shape != self.shape):
slices_large, slices_small = self._overlap_slices(data.shape)
if slices_small is None:
return None # no overlap
# cutout is a copy
cutout = np.zeros(self.shape, dtype=data.dtype)
cutout[:] = fill_value
cutout[slices_small] = data[slices_large]
if isinstance(data, u.Quantity):
cutout = u.Quantity(cutout, unit=data.unit)
return cutout | python | def cutout(self, data, fill_value=0., copy=False):
"""
Create a cutout from the input data over the mask bounding box,
taking any edge effects into account.
Parameters
----------
data : array_like
A 2D array on which to apply the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
does not overlap with the input ``data``. The default is 0.
copy : bool, optional
If `True` then the returned cutout array will always be hold
a copy of the input ``data``. If `False` and the mask is
fully within the input ``data``, then the returned cutout
array will be a view into the input ``data``. In cases
where the mask partially overlaps or has no overlap with the
input ``data``, the returned cutout array will always hold a
copy of the input ``data`` (i.e. this keyword has no
effect).
Returns
-------
result : `~numpy.ndarray`
A 2D array cut out from the input ``data`` representing the
same cutout region as the aperture mask. If there is a
partial overlap of the aperture mask with the input data,
pixels outside of the data will be assigned to
``fill_value``. `None` is returned if there is no overlap
of the aperture with the input ``data``.
"""
data = np.asanyarray(data)
if data.ndim != 2:
raise ValueError('data must be a 2D array.')
partial_overlap = False
if self.bbox.ixmin < 0 or self.bbox.iymin < 0:
partial_overlap = True
if not partial_overlap:
# try this for speed -- the result may still be a partial
# overlap, in which case the next block will be triggered
if copy:
cutout = np.copy(data[self.bbox.slices])
else:
cutout = data[self.bbox.slices]
if partial_overlap or (cutout.shape != self.shape):
slices_large, slices_small = self._overlap_slices(data.shape)
if slices_small is None:
return None # no overlap
# cutout is a copy
cutout = np.zeros(self.shape, dtype=data.dtype)
cutout[:] = fill_value
cutout[slices_small] = data[slices_large]
if isinstance(data, u.Quantity):
cutout = u.Quantity(cutout, unit=data.unit)
return cutout | [
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A 2D array on which to apply the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
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-------
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A 2D array cut out from the input ``data`` representing the
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astropy/photutils | photutils/aperture/mask.py | ApertureMask.multiply | def multiply(self, data, fill_value=0.):
"""
Multiply the aperture mask with the input data, taking any edge
effects into account.
The result is a mask-weighted cutout from the data.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The 2D array to multiply with the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
does not overlap with the input ``data``. The default is 0.
Returns
-------
result : `~numpy.ndarray`
A 2D mask-weighted cutout from the input ``data``. If there
is a partial overlap of the aperture mask with the input
data, pixels outside of the data will be assigned to
``fill_value`` before being multipled with the mask. `None`
is returned if there is no overlap of the aperture with the
input ``data``.
"""
cutout = self.cutout(data, fill_value=fill_value)
if cutout is None:
return None
else:
return cutout * self.data | python | def multiply(self, data, fill_value=0.):
"""
Multiply the aperture mask with the input data, taking any edge
effects into account.
The result is a mask-weighted cutout from the data.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The 2D array to multiply with the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
does not overlap with the input ``data``. The default is 0.
Returns
-------
result : `~numpy.ndarray`
A 2D mask-weighted cutout from the input ``data``. If there
is a partial overlap of the aperture mask with the input
data, pixels outside of the data will be assigned to
``fill_value`` before being multipled with the mask. `None`
is returned if there is no overlap of the aperture with the
input ``data``.
"""
cutout = self.cutout(data, fill_value=fill_value)
if cutout is None:
return None
else:
return cutout * self.data | [
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The 2D array to multiply with the aperture mask.
fill_value : float, optional
The value is used to fill pixels where the aperture mask
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A 2D mask-weighted cutout from the input ``data``. If there
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astropy/photutils | photutils/segmentation/deblend.py | deblend_sources | def deblend_sources(data, segment_img, npixels, filter_kernel=None,
labels=None, nlevels=32, contrast=0.001,
mode='exponential', connectivity=8, relabel=True):
"""
Deblend overlapping sources labeled in a segmentation image.
Sources are deblended using a combination of multi-thresholding and
`watershed segmentation
<https://en.wikipedia.org/wiki/Watershed_(image_processing)>`_. In
order to deblend sources, they must be separated enough such that
there is a saddle between them.
Parameters
----------
data : array_like
The 2D array of the image.
segment_img : `~photutils.segmentation.SegmentationImage` or array_like (int)
A 2D segmentation image, either as a
`~photutils.segmentation.SegmentationImage` object or an
`~numpy.ndarray`, with the same shape as ``data`` where sources
are labeled by different positive integer values. A value of
zero is reserved for the background.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
labels : int or array-like of int, optional
The label numbers to deblend. If `None` (default), then all
labels in the segmentation image will be deblended.
nlevels : int, optional
The number of multi-thresholding levels to use. Each source
will be re-thresholded at ``nlevels``, spaced exponentially or
linearly (see the ``mode`` keyword), between its minimum and
maximum values within the source segment.
contrast : float, optional
The fraction of the total (blended) source flux that a local
peak must have to be considered as a separate object.
``contrast`` must be between 0 and 1, inclusive. If ``contrast
= 0`` then every local peak will be made a separate object
(maximum deblending). If ``contrast = 1`` then no deblending
will occur. The default is 0.001, which will deblend sources
with a magnitude difference of about 7.5.
mode : {'exponential', 'linear'}, optional
The mode used in defining the spacing between the
multi-thresholding levels (see the ``nlevels`` keyword). The
default is 'exponential'.
connectivity : {8, 4}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 8 (default)
or 4. 8-connected pixels touch along their edges or corners.
4-connected pixels touch along their edges. For reference,
SExtractor uses 8-connected pixels.
relabel : bool
If `True` (default), then the segmentation image will be
relabeled such that the labels are in consecutive order starting
from 1.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background.
See Also
--------
:func:`photutils.detect_sources`
"""
if not isinstance(segment_img, SegmentationImage):
segment_img = SegmentationImage(segment_img)
if segment_img.shape != data.shape:
raise ValueError('The data and segmentation image must have '
'the same shape')
if labels is None:
labels = segment_img.labels
labels = np.atleast_1d(labels)
segment_img.check_labels(labels)
data = filter_data(data, filter_kernel, mode='constant', fill_value=0.0)
last_label = segment_img.max_label
segm_deblended = deepcopy(segment_img)
for label in labels:
source_slice = segment_img.slices[segment_img.get_index(label)]
source_data = data[source_slice]
source_segm = SegmentationImage(np.copy(
segment_img.data[source_slice]))
source_segm.keep_labels(label) # include only one label
source_deblended = _deblend_source(
source_data, source_segm, npixels, nlevels=nlevels,
contrast=contrast, mode=mode, connectivity=connectivity)
if not np.array_equal(source_deblended.data.astype(bool),
source_segm.data.astype(bool)):
raise ValueError('Deblending failed for source "{0}". Please '
'ensure you used the same pixel connectivity '
'in detect_sources and deblend_sources. If '
'this issue persists, then please inform the '
'developers.'.format(label))
if source_deblended.nlabels > 1:
# replace the original source with the deblended source
source_mask = (source_deblended.data > 0)
segm_tmp = segm_deblended.data
segm_tmp[source_slice][source_mask] = (
source_deblended.data[source_mask] + last_label)
segm_deblended.data = segm_tmp # needed to call data setter
last_label += source_deblended.nlabels
if relabel:
segm_deblended.relabel_consecutive()
return segm_deblended | python | def deblend_sources(data, segment_img, npixels, filter_kernel=None,
labels=None, nlevels=32, contrast=0.001,
mode='exponential', connectivity=8, relabel=True):
"""
Deblend overlapping sources labeled in a segmentation image.
Sources are deblended using a combination of multi-thresholding and
`watershed segmentation
<https://en.wikipedia.org/wiki/Watershed_(image_processing)>`_. In
order to deblend sources, they must be separated enough such that
there is a saddle between them.
Parameters
----------
data : array_like
The 2D array of the image.
segment_img : `~photutils.segmentation.SegmentationImage` or array_like (int)
A 2D segmentation image, either as a
`~photutils.segmentation.SegmentationImage` object or an
`~numpy.ndarray`, with the same shape as ``data`` where sources
are labeled by different positive integer values. A value of
zero is reserved for the background.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
labels : int or array-like of int, optional
The label numbers to deblend. If `None` (default), then all
labels in the segmentation image will be deblended.
nlevels : int, optional
The number of multi-thresholding levels to use. Each source
will be re-thresholded at ``nlevels``, spaced exponentially or
linearly (see the ``mode`` keyword), between its minimum and
maximum values within the source segment.
contrast : float, optional
The fraction of the total (blended) source flux that a local
peak must have to be considered as a separate object.
``contrast`` must be between 0 and 1, inclusive. If ``contrast
= 0`` then every local peak will be made a separate object
(maximum deblending). If ``contrast = 1`` then no deblending
will occur. The default is 0.001, which will deblend sources
with a magnitude difference of about 7.5.
mode : {'exponential', 'linear'}, optional
The mode used in defining the spacing between the
multi-thresholding levels (see the ``nlevels`` keyword). The
default is 'exponential'.
connectivity : {8, 4}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 8 (default)
or 4. 8-connected pixels touch along their edges or corners.
4-connected pixels touch along their edges. For reference,
SExtractor uses 8-connected pixels.
relabel : bool
If `True` (default), then the segmentation image will be
relabeled such that the labels are in consecutive order starting
from 1.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background.
See Also
--------
:func:`photutils.detect_sources`
"""
if not isinstance(segment_img, SegmentationImage):
segment_img = SegmentationImage(segment_img)
if segment_img.shape != data.shape:
raise ValueError('The data and segmentation image must have '
'the same shape')
if labels is None:
labels = segment_img.labels
labels = np.atleast_1d(labels)
segment_img.check_labels(labels)
data = filter_data(data, filter_kernel, mode='constant', fill_value=0.0)
last_label = segment_img.max_label
segm_deblended = deepcopy(segment_img)
for label in labels:
source_slice = segment_img.slices[segment_img.get_index(label)]
source_data = data[source_slice]
source_segm = SegmentationImage(np.copy(
segment_img.data[source_slice]))
source_segm.keep_labels(label) # include only one label
source_deblended = _deblend_source(
source_data, source_segm, npixels, nlevels=nlevels,
contrast=contrast, mode=mode, connectivity=connectivity)
if not np.array_equal(source_deblended.data.astype(bool),
source_segm.data.astype(bool)):
raise ValueError('Deblending failed for source "{0}". Please '
'ensure you used the same pixel connectivity '
'in detect_sources and deblend_sources. If '
'this issue persists, then please inform the '
'developers.'.format(label))
if source_deblended.nlabels > 1:
# replace the original source with the deblended source
source_mask = (source_deblended.data > 0)
segm_tmp = segm_deblended.data
segm_tmp[source_slice][source_mask] = (
source_deblended.data[source_mask] + last_label)
segm_deblended.data = segm_tmp # needed to call data setter
last_label += source_deblended.nlabels
if relabel:
segm_deblended.relabel_consecutive()
return segm_deblended | [
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Sources are deblended using a combination of multi-thresholding and
`watershed segmentation
<https://en.wikipedia.org/wiki/Watershed_(image_processing)>`_. In
order to deblend sources, they must be separated enough such that
there is a saddle between them.
Parameters
----------
data : array_like
The 2D array of the image.
segment_img : `~photutils.segmentation.SegmentationImage` or array_like (int)
A 2D segmentation image, either as a
`~photutils.segmentation.SegmentationImage` object or an
`~numpy.ndarray`, with the same shape as ``data`` where sources
are labeled by different positive integer values. A value of
zero is reserved for the background.
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the image before
thresholding. Filtering the image will smooth the noise and
maximize detectability of objects with a shape similar to the
kernel.
labels : int or array-like of int, optional
The label numbers to deblend. If `None` (default), then all
labels in the segmentation image will be deblended.
nlevels : int, optional
The number of multi-thresholding levels to use. Each source
will be re-thresholded at ``nlevels``, spaced exponentially or
linearly (see the ``mode`` keyword), between its minimum and
maximum values within the source segment.
contrast : float, optional
The fraction of the total (blended) source flux that a local
peak must have to be considered as a separate object.
``contrast`` must be between 0 and 1, inclusive. If ``contrast
= 0`` then every local peak will be made a separate object
(maximum deblending). If ``contrast = 1`` then no deblending
will occur. The default is 0.001, which will deblend sources
with a magnitude difference of about 7.5.
mode : {'exponential', 'linear'}, optional
The mode used in defining the spacing between the
multi-thresholding levels (see the ``nlevels`` keyword). The
default is 'exponential'.
connectivity : {8, 4}, optional
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 8 (default)
or 4. 8-connected pixels touch along their edges or corners.
4-connected pixels touch along their edges. For reference,
SExtractor uses 8-connected pixels.
relabel : bool
If `True` (default), then the segmentation image will be
relabeled such that the labels are in consecutive order starting
from 1.
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background.
See Also
--------
:func:`photutils.detect_sources` | [
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astropy/photutils | photutils/utils/_moments.py | _moments_central | def _moments_central(data, center=None, order=1):
"""
Calculate the central image moments up to the specified order.
Parameters
----------
data : 2D array-like
The input 2D array.
center : tuple of two floats or `None`, optional
The ``(x, y)`` center position. If `None` it will calculated as
the "center of mass" of the input ``data``.
order : int, optional
The maximum order of the moments to calculate.
Returns
-------
moments : 2D `~numpy.ndarray`
The central image moments.
"""
data = np.asarray(data).astype(float)
if data.ndim != 2:
raise ValueError('data must be a 2D array.')
if center is None:
from ..centroids import centroid_com
center = centroid_com(data)
indices = np.ogrid[[slice(0, i) for i in data.shape]]
ypowers = (indices[0] - center[1]) ** np.arange(order + 1)
xpowers = np.transpose(indices[1] - center[0]) ** np.arange(order + 1)
return np.dot(np.dot(np.transpose(ypowers), data), xpowers) | python | def _moments_central(data, center=None, order=1):
"""
Calculate the central image moments up to the specified order.
Parameters
----------
data : 2D array-like
The input 2D array.
center : tuple of two floats or `None`, optional
The ``(x, y)`` center position. If `None` it will calculated as
the "center of mass" of the input ``data``.
order : int, optional
The maximum order of the moments to calculate.
Returns
-------
moments : 2D `~numpy.ndarray`
The central image moments.
"""
data = np.asarray(data).astype(float)
if data.ndim != 2:
raise ValueError('data must be a 2D array.')
if center is None:
from ..centroids import centroid_com
center = centroid_com(data)
indices = np.ogrid[[slice(0, i) for i in data.shape]]
ypowers = (indices[0] - center[1]) ** np.arange(order + 1)
xpowers = np.transpose(indices[1] - center[0]) ** np.arange(order + 1)
return np.dot(np.dot(np.transpose(ypowers), data), xpowers) | [
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order : int, optional
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astropy/photutils | photutils/isophote/harmonics.py | first_and_second_harmonic_function | def first_and_second_harmonic_function(phi, c):
"""
Compute the harmonic function value used to calculate the
corrections for ellipse fitting.
This function includes simultaneously both the first and second
order harmonics:
.. math::
f(phi) = c[0] + c[1]*\\sin(phi) + c[2]*\\cos(phi) +
c[3]*\\sin(2*phi) + c[4]*\\cos(2*phi)
Parameters
----------
phi : float or `~numpy.ndarray`
The angle(s) along the elliptical path, going towards the positive
y axis, starting coincident with the position angle. That is, the
angles are defined from the semimajor axis that lies in
the positive x quadrant.
c : `~numpy.ndarray` of shape (5,)
Array containing the five harmonic coefficients.
Returns
-------
result : float or `~numpy.ndarray`
The function value(s) at the given input angle(s).
"""
return (c[0] + c[1]*np.sin(phi) + c[2]*np.cos(phi) + c[3]*np.sin(2*phi) +
c[4]*np.cos(2*phi)) | python | def first_and_second_harmonic_function(phi, c):
"""
Compute the harmonic function value used to calculate the
corrections for ellipse fitting.
This function includes simultaneously both the first and second
order harmonics:
.. math::
f(phi) = c[0] + c[1]*\\sin(phi) + c[2]*\\cos(phi) +
c[3]*\\sin(2*phi) + c[4]*\\cos(2*phi)
Parameters
----------
phi : float or `~numpy.ndarray`
The angle(s) along the elliptical path, going towards the positive
y axis, starting coincident with the position angle. That is, the
angles are defined from the semimajor axis that lies in
the positive x quadrant.
c : `~numpy.ndarray` of shape (5,)
Array containing the five harmonic coefficients.
Returns
-------
result : float or `~numpy.ndarray`
The function value(s) at the given input angle(s).
"""
return (c[0] + c[1]*np.sin(phi) + c[2]*np.cos(phi) + c[3]*np.sin(2*phi) +
c[4]*np.cos(2*phi)) | [
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astropy/photutils | photutils/psf/matching/windows.py | _radial_distance | def _radial_distance(shape):
"""
Return an array where each value is the Euclidean distance from the
array center.
Parameters
----------
shape : tuple of int
The size of the output array along each axis.
Returns
-------
result : `~numpy.ndarray`
An array containing the Euclidian radial distances from the
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"""
if len(shape) != 2:
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x = np.arange(shape[1]) - position[1]
y = np.arange(shape[0]) - position[0]
xx, yy = np.meshgrid(x, y)
return np.sqrt(xx**2 + yy**2) | python | def _radial_distance(shape):
"""
Return an array where each value is the Euclidean distance from the
array center.
Parameters
----------
shape : tuple of int
The size of the output array along each axis.
Returns
-------
result : `~numpy.ndarray`
An array containing the Euclidian radial distances from the
array center.
"""
if len(shape) != 2:
raise ValueError('shape must have only 2 elements')
position = (np.asarray(shape) - 1) / 2.
x = np.arange(shape[1]) - position[1]
y = np.arange(shape[0]) - position[0]
xx, yy = np.meshgrid(x, y)
return np.sqrt(xx**2 + yy**2) | [
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astropy/photutils | photutils/datasets/load.py | load_spitzer_image | def load_spitzer_image(show_progress=False): # pragma: no cover
"""
Load a 4.5 micron Spitzer image.
The catalog for this image is returned by
:func:`load_spitzer_catalog`.
Parameters
----------
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The 4.5 micron Spitzer image in a FITS image HDU.
See Also
--------
load_spitzer_catalog
Examples
--------
.. plot::
:include-source:
from photutils import datasets
hdu = datasets.load_spitzer_image()
plt.imshow(hdu.data, origin='lower', vmax=50)
"""
path = get_path('spitzer_example_image.fits', location='remote',
show_progress=show_progress)
hdu = fits.open(path)[0]
return hdu | python | def load_spitzer_image(show_progress=False): # pragma: no cover
"""
Load a 4.5 micron Spitzer image.
The catalog for this image is returned by
:func:`load_spitzer_catalog`.
Parameters
----------
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The 4.5 micron Spitzer image in a FITS image HDU.
See Also
--------
load_spitzer_catalog
Examples
--------
.. plot::
:include-source:
from photutils import datasets
hdu = datasets.load_spitzer_image()
plt.imshow(hdu.data, origin='lower', vmax=50)
"""
path = get_path('spitzer_example_image.fits', location='remote',
show_progress=show_progress)
hdu = fits.open(path)[0]
return hdu | [
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show_progress : bool, optional
Whether to display a progress bar during the download (default
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Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The 4.5 micron Spitzer image in a FITS image HDU.
See Also
--------
load_spitzer_catalog
Examples
--------
.. plot::
:include-source:
from photutils import datasets
hdu = datasets.load_spitzer_image()
plt.imshow(hdu.data, origin='lower', vmax=50) | [
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astropy/photutils | photutils/datasets/load.py | load_spitzer_catalog | def load_spitzer_catalog(show_progress=False): # pragma: no cover
"""
Load a 4.5 micron Spitzer catalog.
The image from which this catalog was derived is returned by
:func:`load_spitzer_image`.
Parameters
----------
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
catalog : `~astropy.table.Table`
The catalog of sources.
See Also
--------
load_spitzer_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
catalog = datasets.load_spitzer_catalog()
plt.scatter(catalog['l'], catalog['b'])
plt.xlabel('Galactic l')
plt.ylabel('Galactic b')
plt.xlim(18.39, 18.05)
plt.ylim(0.13, 0.30)
"""
path = get_path('spitzer_example_catalog.xml', location='remote',
show_progress=show_progress)
table = Table.read(path)
return table | python | def load_spitzer_catalog(show_progress=False): # pragma: no cover
"""
Load a 4.5 micron Spitzer catalog.
The image from which this catalog was derived is returned by
:func:`load_spitzer_image`.
Parameters
----------
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
catalog : `~astropy.table.Table`
The catalog of sources.
See Also
--------
load_spitzer_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
catalog = datasets.load_spitzer_catalog()
plt.scatter(catalog['l'], catalog['b'])
plt.xlabel('Galactic l')
plt.ylabel('Galactic b')
plt.xlim(18.39, 18.05)
plt.ylim(0.13, 0.30)
"""
path = get_path('spitzer_example_catalog.xml', location='remote',
show_progress=show_progress)
table = Table.read(path)
return table | [
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The image from which this catalog was derived is returned by
:func:`load_spitzer_image`.
Parameters
----------
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
catalog : `~astropy.table.Table`
The catalog of sources.
See Also
--------
load_spitzer_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
catalog = datasets.load_spitzer_catalog()
plt.scatter(catalog['l'], catalog['b'])
plt.xlabel('Galactic l')
plt.ylabel('Galactic b')
plt.xlim(18.39, 18.05)
plt.ylim(0.13, 0.30) | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/datasets/load.py#L112-L152 | train |
astropy/photutils | photutils/datasets/load.py | load_irac_psf | def load_irac_psf(channel, show_progress=False): # pragma: no cover
"""
Load a Spitzer IRAC PSF image.
Parameters
----------
channel : int (1-4)
The IRAC channel number:
* Channel 1: 3.6 microns
* Channel 2: 4.5 microns
* Channel 3: 5.8 microns
* Channel 4: 8.0 microns
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The IRAC PSF in a FITS image HDU.
Examples
--------
.. plot::
:include-source:
from astropy.visualization import LogStretch, ImageNormalize
from photutils.datasets import load_irac_psf
hdu1 = load_irac_psf(1)
hdu2 = load_irac_psf(2)
hdu3 = load_irac_psf(3)
hdu4 = load_irac_psf(4)
norm = ImageNormalize(hdu1.data, stretch=LogStretch())
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(hdu1.data, origin='lower', interpolation='nearest',
norm=norm)
ax1.set_title('IRAC Ch1 PSF')
ax2.imshow(hdu2.data, origin='lower', interpolation='nearest',
norm=norm)
ax2.set_title('IRAC Ch2 PSF')
ax3.imshow(hdu3.data, origin='lower', interpolation='nearest',
norm=norm)
ax3.set_title('IRAC Ch3 PSF')
ax4.imshow(hdu4.data, origin='lower', interpolation='nearest',
norm=norm)
ax4.set_title('IRAC Ch4 PSF')
plt.tight_layout()
plt.show()
"""
channel = int(channel)
if channel < 1 or channel > 4:
raise ValueError('channel must be 1, 2, 3, or 4')
fn = 'irac_ch{0}_flight.fits'.format(channel)
path = get_path(fn, location='remote', show_progress=show_progress)
hdu = fits.open(path)[0]
return hdu | python | def load_irac_psf(channel, show_progress=False): # pragma: no cover
"""
Load a Spitzer IRAC PSF image.
Parameters
----------
channel : int (1-4)
The IRAC channel number:
* Channel 1: 3.6 microns
* Channel 2: 4.5 microns
* Channel 3: 5.8 microns
* Channel 4: 8.0 microns
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The IRAC PSF in a FITS image HDU.
Examples
--------
.. plot::
:include-source:
from astropy.visualization import LogStretch, ImageNormalize
from photutils.datasets import load_irac_psf
hdu1 = load_irac_psf(1)
hdu2 = load_irac_psf(2)
hdu3 = load_irac_psf(3)
hdu4 = load_irac_psf(4)
norm = ImageNormalize(hdu1.data, stretch=LogStretch())
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(hdu1.data, origin='lower', interpolation='nearest',
norm=norm)
ax1.set_title('IRAC Ch1 PSF')
ax2.imshow(hdu2.data, origin='lower', interpolation='nearest',
norm=norm)
ax2.set_title('IRAC Ch2 PSF')
ax3.imshow(hdu3.data, origin='lower', interpolation='nearest',
norm=norm)
ax3.set_title('IRAC Ch3 PSF')
ax4.imshow(hdu4.data, origin='lower', interpolation='nearest',
norm=norm)
ax4.set_title('IRAC Ch4 PSF')
plt.tight_layout()
plt.show()
"""
channel = int(channel)
if channel < 1 or channel > 4:
raise ValueError('channel must be 1, 2, 3, or 4')
fn = 'irac_ch{0}_flight.fits'.format(channel)
path = get_path(fn, location='remote', show_progress=show_progress)
hdu = fits.open(path)[0]
return hdu | [
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Parameters
----------
channel : int (1-4)
The IRAC channel number:
* Channel 1: 3.6 microns
* Channel 2: 4.5 microns
* Channel 3: 5.8 microns
* Channel 4: 8.0 microns
show_progress : bool, optional
Whether to display a progress bar during the download (default
is `False`).
Returns
-------
hdu : `~astropy.io.fits.ImageHDU`
The IRAC PSF in a FITS image HDU.
Examples
--------
.. plot::
:include-source:
from astropy.visualization import LogStretch, ImageNormalize
from photutils.datasets import load_irac_psf
hdu1 = load_irac_psf(1)
hdu2 = load_irac_psf(2)
hdu3 = load_irac_psf(3)
hdu4 = load_irac_psf(4)
norm = ImageNormalize(hdu1.data, stretch=LogStretch())
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(hdu1.data, origin='lower', interpolation='nearest',
norm=norm)
ax1.set_title('IRAC Ch1 PSF')
ax2.imshow(hdu2.data, origin='lower', interpolation='nearest',
norm=norm)
ax2.set_title('IRAC Ch2 PSF')
ax3.imshow(hdu3.data, origin='lower', interpolation='nearest',
norm=norm)
ax3.set_title('IRAC Ch3 PSF')
ax4.imshow(hdu4.data, origin='lower', interpolation='nearest',
norm=norm)
ax4.set_title('IRAC Ch4 PSF')
plt.tight_layout()
plt.show() | [
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] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/datasets/load.py#L155-L217 | train |
astropy/photutils | photutils/isophote/ellipse.py | Ellipse.fit_image | def fit_image(self, sma0=None, minsma=0., maxsma=None, step=0.1,
conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT,
maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG,
maxgerr=DEFAULT_MAXGERR, sclip=3., nclip=0,
integrmode=BILINEAR, linear=False, maxrit=None):
# 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.
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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 interations. 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.
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.
else:
sma = sma0
# 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)
# 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 = self.fit_isophote(0.0, isophote_list=isophote_list)
# sort list of isophotes according to sma
isophote_list.sort()
return IsophoteList(isophote_list) | python | def fit_image(self, sma0=None, minsma=0., maxsma=None, step=0.1,
conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT,
maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG,
maxgerr=DEFAULT_MAXGERR, sclip=3., nclip=0,
integrmode=BILINEAR, linear=False, maxrit=None):
# 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.
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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 interations. 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.
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.
else:
sma = sma0
# 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)
# 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 = self.fit_isophote(0.0, isophote_list=isophote_list)
# sort list of isophotes according to sma
isophote_list.sort()
return IsophoteList(isophote_list) | [
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] | 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.
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)
<http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ 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 interations. 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.
Returns
-------
result : `~photutils.isophote.IsophoteList` instance
A list-like object of `~photutils.isophote.Isophote`
instances, sorted by increasing semimajor axis length. | [
"Fit",
"multiple",
"isophotes",
"to",
"the",
"image",
"array",
"."
] | cc9bb4534ab76bac98cb5f374a348a2573d10401 | https://github.com/astropy/photutils/blob/cc9bb4534ab76bac98cb5f374a348a2573d10401/photutils/isophote/ellipse.py#L199-L449 | train |
astropy/photutils | photutils/isophote/ellipse.py | Ellipse.fit_isophote | 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., 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 paramter 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 interations. 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 is not None and len(isophote_list) > 0:
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 | python | 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., 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 paramter 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 interations. 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 is not None and len(isophote_list) > 0:
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 | [
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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 paramter 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 interations. 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
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astropy/photutils | photutils/aperture/circle.py | CircularAperture.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyCircularAperture` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyCircularAperture` object
A `SkyCircularAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyCircularAperture(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyCircularAperture` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyCircularAperture` object
A `SkyCircularAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyCircularAperture(**sky_params) | [
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astropy/photutils | photutils/aperture/circle.py | CircularAnnulus.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyCircularAnnulus` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
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aperture : `SkyCircularAnnulus` object
A `SkyCircularAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyCircularAnnulus(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyCircularAnnulus` object defined
in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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transformation (``'wcs'``).
Returns
-------
aperture : `SkyCircularAnnulus` object
A `SkyCircularAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
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astropy/photutils | photutils/aperture/circle.py | SkyCircularAperture.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `CircularAperture` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
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aperture : `CircularAperture` object
A `CircularAperture` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return CircularAperture(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `CircularAperture` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
-------
aperture : `CircularAperture` object
A `CircularAperture` object.
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astropy/photutils | photutils/aperture/circle.py | SkyCircularAnnulus.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `CircularAnnulus` object defined in
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Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
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Whether to do the transformation including distortions
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-------
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A `CircularAnnulus` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return CircularAnnulus(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `CircularAnnulus` object defined in
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Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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-------
aperture : `CircularAnnulus` object
A `CircularAnnulus` object.
"""
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astropy/photutils | photutils/datasets/make.py | apply_poisson_noise | def apply_poisson_noise(data, random_state=None):
"""
Apply Poisson noise to an array, where the value of each element in
the input array represents the expected number of counts.
Each pixel in the output array is generated by drawing a random
sample from a Poisson distribution whose expectation value is given
by the pixel value in the input array.
Parameters
----------
data : array-like
The array on which to apply Poisson noise. Every pixel in the
array must have a positive value (i.e. counts).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
result : `~numpy.ndarray`
The data array after applying Poisson noise.
See Also
--------
make_noise_image
Examples
--------
.. plot::
:include-source:
from photutils.datasets import make_4gaussians_image
from photutils.datasets import apply_poisson_noise
data1 = make_4gaussians_image(noise=False)
data2 = apply_poisson_noise(data1, random_state=12345)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(data1, origin='lower', interpolation='nearest')
ax1.set_title('Original image')
ax2.imshow(data2, origin='lower', interpolation='nearest')
ax2.set_title('Original image with Poisson noise applied')
"""
data = np.asanyarray(data)
if np.any(data < 0):
raise ValueError('data must not contain any negative values')
prng = check_random_state(random_state)
return prng.poisson(data) | python | def apply_poisson_noise(data, random_state=None):
"""
Apply Poisson noise to an array, where the value of each element in
the input array represents the expected number of counts.
Each pixel in the output array is generated by drawing a random
sample from a Poisson distribution whose expectation value is given
by the pixel value in the input array.
Parameters
----------
data : array-like
The array on which to apply Poisson noise. Every pixel in the
array must have a positive value (i.e. counts).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
result : `~numpy.ndarray`
The data array after applying Poisson noise.
See Also
--------
make_noise_image
Examples
--------
.. plot::
:include-source:
from photutils.datasets import make_4gaussians_image
from photutils.datasets import apply_poisson_noise
data1 = make_4gaussians_image(noise=False)
data2 = apply_poisson_noise(data1, random_state=12345)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(data1, origin='lower', interpolation='nearest')
ax1.set_title('Original image')
ax2.imshow(data2, origin='lower', interpolation='nearest')
ax2.set_title('Original image with Poisson noise applied')
"""
data = np.asanyarray(data)
if np.any(data < 0):
raise ValueError('data must not contain any negative values')
prng = check_random_state(random_state)
return prng.poisson(data) | [
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The array on which to apply Poisson noise. Every pixel in the
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random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
result : `~numpy.ndarray`
The data array after applying Poisson noise.
See Also
--------
make_noise_image
Examples
--------
.. plot::
:include-source:
from photutils.datasets import make_4gaussians_image
from photutils.datasets import apply_poisson_noise
data1 = make_4gaussians_image(noise=False)
data2 = apply_poisson_noise(data1, random_state=12345)
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import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(data1, origin='lower', interpolation='nearest')
ax1.set_title('Original image')
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astropy/photutils | photutils/datasets/make.py | make_noise_image | def make_noise_image(shape, type='gaussian', mean=None, stddev=None,
random_state=None):
"""
Make a noise image containing Gaussian or Poisson noise.
Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
type : {'gaussian', 'poisson'}
The distribution used to generate the random noise:
* ``'gaussian'``: Gaussian distributed noise.
* ``'poisson'``: Poisson distributed noise.
mean : float
The mean of the random distribution. Required for both Gaussian
and Poisson noise. The default is 0.
stddev : float, optional
The standard deviation of the Gaussian noise to add to the
output image. Required for Gaussian noise and ignored for
Poisson noise (the variance of the Poisson distribution is equal
to its mean).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Separate function calls with the same noise parameters and
``random_state`` will generate the identical noise image.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing random noise.
See Also
--------
apply_poisson_noise
Examples
--------
.. plot::
:include-source:
# make Gaussian and Poisson noise images
from photutils.datasets import make_noise_image
shape = (100, 100)
image1 = make_noise_image(shape, type='gaussian', mean=0., stddev=5.)
image2 = make_noise_image(shape, type='poisson', mean=5.)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax1.set_title('Gaussian noise ($\\mu=0$, $\\sigma=5.$)')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax2.set_title('Poisson noise ($\\mu=5$)')
"""
if mean is None:
raise ValueError('"mean" must be input')
prng = check_random_state(random_state)
if type == 'gaussian':
if stddev is None:
raise ValueError('"stddev" must be input for Gaussian noise')
image = prng.normal(loc=mean, scale=stddev, size=shape)
elif type == 'poisson':
image = prng.poisson(lam=mean, size=shape)
else:
raise ValueError('Invalid type: {0}. Use one of '
'{"gaussian", "poisson"}.'.format(type))
return image | python | def make_noise_image(shape, type='gaussian', mean=None, stddev=None,
random_state=None):
"""
Make a noise image containing Gaussian or Poisson noise.
Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
type : {'gaussian', 'poisson'}
The distribution used to generate the random noise:
* ``'gaussian'``: Gaussian distributed noise.
* ``'poisson'``: Poisson distributed noise.
mean : float
The mean of the random distribution. Required for both Gaussian
and Poisson noise. The default is 0.
stddev : float, optional
The standard deviation of the Gaussian noise to add to the
output image. Required for Gaussian noise and ignored for
Poisson noise (the variance of the Poisson distribution is equal
to its mean).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Separate function calls with the same noise parameters and
``random_state`` will generate the identical noise image.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing random noise.
See Also
--------
apply_poisson_noise
Examples
--------
.. plot::
:include-source:
# make Gaussian and Poisson noise images
from photutils.datasets import make_noise_image
shape = (100, 100)
image1 = make_noise_image(shape, type='gaussian', mean=0., stddev=5.)
image2 = make_noise_image(shape, type='poisson', mean=5.)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax1.set_title('Gaussian noise ($\\mu=0$, $\\sigma=5.$)')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax2.set_title('Poisson noise ($\\mu=5$)')
"""
if mean is None:
raise ValueError('"mean" must be input')
prng = check_random_state(random_state)
if type == 'gaussian':
if stddev is None:
raise ValueError('"stddev" must be input for Gaussian noise')
image = prng.normal(loc=mean, scale=stddev, size=shape)
elif type == 'poisson':
image = prng.poisson(lam=mean, size=shape)
else:
raise ValueError('Invalid type: {0}. Use one of '
'{"gaussian", "poisson"}.'.format(type))
return image | [
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Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
type : {'gaussian', 'poisson'}
The distribution used to generate the random noise:
* ``'gaussian'``: Gaussian distributed noise.
* ``'poisson'``: Poisson distributed noise.
mean : float
The mean of the random distribution. Required for both Gaussian
and Poisson noise. The default is 0.
stddev : float, optional
The standard deviation of the Gaussian noise to add to the
output image. Required for Gaussian noise and ignored for
Poisson noise (the variance of the Poisson distribution is equal
to its mean).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Separate function calls with the same noise parameters and
``random_state`` will generate the identical noise image.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing random noise.
See Also
--------
apply_poisson_noise
Examples
--------
.. plot::
:include-source:
# make Gaussian and Poisson noise images
from photutils.datasets import make_noise_image
shape = (100, 100)
image1 = make_noise_image(shape, type='gaussian', mean=0., stddev=5.)
image2 = make_noise_image(shape, type='poisson', mean=5.)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax1.set_title('Gaussian noise ($\\mu=0$, $\\sigma=5.$)')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax2.set_title('Poisson noise ($\\mu=5$)') | [
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astropy/photutils | photutils/datasets/make.py | make_random_models_table | def make_random_models_table(n_sources, param_ranges, random_state=None):
"""
Make a `~astropy.table.Table` containing randomly generated
parameters for an Astropy model to simulate a set of sources.
Each row of the table corresponds to a source whose parameters are
defined by the column names. The parameters are drawn from a
uniform distribution over the specified input ranges.
The output table can be input into :func:`make_model_sources_image`
to create an image containing the model sources.
Parameters
----------
n_sources : float
The number of random model sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the model parameters
as a `dict` mapping the parameter name to its ``(lower, upper)``
bounds.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated sources. Each
row of the table corresponds to a source whose model parameters
are defined by the column names. The column names will be the
keys of the dictionary ``param_ranges``.
See Also
--------
make_random_gaussians_table, make_model_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_models_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_models_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298
"""
prng = check_random_state(random_state)
sources = Table()
for param_name, (lower, upper) in param_ranges.items():
# Generate a column for every item in param_ranges, even if it
# is not in the model (e.g. flux). However, such columns will
# be ignored when rendering the image.
sources[param_name] = prng.uniform(lower, upper, n_sources)
return sources | python | def make_random_models_table(n_sources, param_ranges, random_state=None):
"""
Make a `~astropy.table.Table` containing randomly generated
parameters for an Astropy model to simulate a set of sources.
Each row of the table corresponds to a source whose parameters are
defined by the column names. The parameters are drawn from a
uniform distribution over the specified input ranges.
The output table can be input into :func:`make_model_sources_image`
to create an image containing the model sources.
Parameters
----------
n_sources : float
The number of random model sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the model parameters
as a `dict` mapping the parameter name to its ``(lower, upper)``
bounds.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated sources. Each
row of the table corresponds to a source whose model parameters
are defined by the column names. The column names will be the
keys of the dictionary ``param_ranges``.
See Also
--------
make_random_gaussians_table, make_model_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_models_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_models_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298
"""
prng = check_random_state(random_state)
sources = Table()
for param_name, (lower, upper) in param_ranges.items():
# Generate a column for every item in param_ranges, even if it
# is not in the model (e.g. flux). However, such columns will
# be ignored when rendering the image.
sources[param_name] = prng.uniform(lower, upper, n_sources)
return sources | [
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Each row of the table corresponds to a source whose parameters are
defined by the column names. The parameters are drawn from a
uniform distribution over the specified input ranges.
The output table can be input into :func:`make_model_sources_image`
to create an image containing the model sources.
Parameters
----------
n_sources : float
The number of random model sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the model parameters
as a `dict` mapping the parameter name to its ``(lower, upper)``
bounds.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated sources. Each
row of the table corresponds to a source whose model parameters
are defined by the column names. The column names will be the
keys of the dictionary ``param_ranges``.
See Also
--------
make_random_gaussians_table, make_model_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_models_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_models_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
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astropy/photutils | photutils/datasets/make.py | make_random_gaussians_table | def make_random_gaussians_table(n_sources, param_ranges, random_state=None):
"""
Make a `~astropy.table.Table` containing randomly generated
parameters for 2D Gaussian sources.
Each row of the table corresponds to a Gaussian source whose
parameters are defined by the column names. The parameters are
drawn from a uniform distribution over the specified input ranges.
The output table can be input into
:func:`make_gaussian_sources_image` to create an image containing
the 2D Gaussian sources.
Parameters
----------
n_sources : float
The number of random Gaussian sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the
`~astropy.modeling.functional_models.Gaussian2D` parameters as a
`dict` mapping the parameter name to its ``(lower, upper)``
bounds. The dictionary keys must be valid
`~astropy.modeling.functional_models.Gaussian2D` parameter names
or ``'flux'``. If ``'flux'`` is specified, but not
``'amplitude'`` then the 2D Gaussian amplitudes will be
calculated and placed in the output table. If both ``'flux'``
and ``'amplitude'`` are specified, then ``'flux'`` will be
ignored. Model parameters not defined in ``param_ranges`` will
be set to the default value.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated Gaussian
sources. Each row of the table corresponds to a Gaussian source
whose parameters are defined by the column names.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_gaussians_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298
To specifying the flux range instead of the amplitude range:
>>> param_ranges = [('flux', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
flux x_mean y_mean x_stddev y_stddev theta amplitude
--------- --------- --------- --------- --------- ---------- ---------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859 11.863685
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889 6.3854388
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615 7.3122209
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422 8.5691781
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298 11.611707
Note that in this case the output table contains both a flux and
amplitude column. The flux column will be ignored when generating
an image of the models using :func:`make_gaussian_sources_image`.
"""
sources = make_random_models_table(n_sources, param_ranges,
random_state=random_state)
# convert Gaussian2D flux to amplitude
if 'flux' in param_ranges and 'amplitude' not in param_ranges:
model = Gaussian2D(x_stddev=1, y_stddev=1)
if 'x_stddev' in sources.colnames:
xstd = sources['x_stddev']
else:
xstd = model.x_stddev.value # default
if 'y_stddev' in sources.colnames:
ystd = sources['y_stddev']
else:
ystd = model.y_stddev.value # default
sources = sources.copy()
sources['amplitude'] = sources['flux'] / (2. * np.pi * xstd * ystd)
return sources | python | def make_random_gaussians_table(n_sources, param_ranges, random_state=None):
"""
Make a `~astropy.table.Table` containing randomly generated
parameters for 2D Gaussian sources.
Each row of the table corresponds to a Gaussian source whose
parameters are defined by the column names. The parameters are
drawn from a uniform distribution over the specified input ranges.
The output table can be input into
:func:`make_gaussian_sources_image` to create an image containing
the 2D Gaussian sources.
Parameters
----------
n_sources : float
The number of random Gaussian sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the
`~astropy.modeling.functional_models.Gaussian2D` parameters as a
`dict` mapping the parameter name to its ``(lower, upper)``
bounds. The dictionary keys must be valid
`~astropy.modeling.functional_models.Gaussian2D` parameter names
or ``'flux'``. If ``'flux'`` is specified, but not
``'amplitude'`` then the 2D Gaussian amplitudes will be
calculated and placed in the output table. If both ``'flux'``
and ``'amplitude'`` are specified, then ``'flux'`` will be
ignored. Model parameters not defined in ``param_ranges`` will
be set to the default value.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated Gaussian
sources. Each row of the table corresponds to a Gaussian source
whose parameters are defined by the column names.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_gaussians_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298
To specifying the flux range instead of the amplitude range:
>>> param_ranges = [('flux', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
flux x_mean y_mean x_stddev y_stddev theta amplitude
--------- --------- --------- --------- --------- ---------- ---------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859 11.863685
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889 6.3854388
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615 7.3122209
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422 8.5691781
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298 11.611707
Note that in this case the output table contains both a flux and
amplitude column. The flux column will be ignored when generating
an image of the models using :func:`make_gaussian_sources_image`.
"""
sources = make_random_models_table(n_sources, param_ranges,
random_state=random_state)
# convert Gaussian2D flux to amplitude
if 'flux' in param_ranges and 'amplitude' not in param_ranges:
model = Gaussian2D(x_stddev=1, y_stddev=1)
if 'x_stddev' in sources.colnames:
xstd = sources['x_stddev']
else:
xstd = model.x_stddev.value # default
if 'y_stddev' in sources.colnames:
ystd = sources['y_stddev']
else:
ystd = model.y_stddev.value # default
sources = sources.copy()
sources['amplitude'] = sources['flux'] / (2. * np.pi * xstd * ystd)
return sources | [
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parameters for 2D Gaussian sources.
Each row of the table corresponds to a Gaussian source whose
parameters are defined by the column names. The parameters are
drawn from a uniform distribution over the specified input ranges.
The output table can be input into
:func:`make_gaussian_sources_image` to create an image containing
the 2D Gaussian sources.
Parameters
----------
n_sources : float
The number of random Gaussian sources to generate.
param_ranges : dict
The lower and upper boundaries for each of the
`~astropy.modeling.functional_models.Gaussian2D` parameters as a
`dict` mapping the parameter name to its ``(lower, upper)``
bounds. The dictionary keys must be valid
`~astropy.modeling.functional_models.Gaussian2D` parameter names
or ``'flux'``. If ``'flux'`` is specified, but not
``'amplitude'`` then the 2D Gaussian amplitudes will be
calculated and placed in the output table. If both ``'flux'``
and ``'amplitude'`` are specified, then ``'flux'`` will be
ignored. Model parameters not defined in ``param_ranges`` will
be set to the default value.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
table : `~astropy.table.Table`
A table of parameters for the randomly generated Gaussian
sources. Each row of the table corresponds to a Gaussian source
whose parameters are defined by the column names.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Notes
-----
To generate identical parameter values from separate function calls,
``param_ranges`` must be input as an `~collections.OrderedDict` with
the same parameter ranges and ``random_state`` must be the same.
Examples
--------
>>> from collections import OrderedDict
>>> from photutils.datasets import make_random_gaussians_table
>>> n_sources = 5
>>> param_ranges = [('amplitude', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
amplitude x_mean y_mean x_stddev y_stddev theta
--------- --------- --------- --------- --------- ----------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298
To specifying the flux range instead of the amplitude range:
>>> param_ranges = [('flux', [500, 1000]),
... ('x_mean', [0, 500]),
... ('y_mean', [0, 300]),
... ('x_stddev', [1, 5]),
... ('y_stddev', [1, 5]),
... ('theta', [0, np.pi])]
>>> param_ranges = OrderedDict(param_ranges)
>>> sources = make_random_gaussians_table(n_sources, param_ranges,
... random_state=12345)
>>> for col in sources.colnames:
... sources[col].info.format = '%.8g' # for consistent table output
>>> print(sources)
flux x_mean y_mean x_stddev y_stddev theta amplitude
--------- --------- --------- --------- --------- ---------- ---------
964.80805 297.77235 224.31444 3.6256447 3.5699013 2.2923859 11.863685
658.18778 482.25726 288.39202 4.2392502 3.8698145 3.1227889 6.3854388
591.95941 326.58855 2.5164894 4.4887037 2.870396 2.1264615 7.3122209
602.28014 374.45332 31.933313 4.8585904 2.3023387 2.4844422 8.5691781
783.86251 326.78494 89.611114 3.8947414 2.7585784 0.53694298 11.611707
Note that in this case the output table contains both a flux and
amplitude column. The flux column will be ignored when generating
an image of the models using :func:`make_gaussian_sources_image`. | [
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astropy/photutils | photutils/datasets/make.py | make_model_sources_image | def make_model_sources_image(shape, model, source_table, oversample=1):
"""
Make an image containing sources generated from a user-specified
model.
Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
model : 2D astropy.modeling.models object
The model to be used for rendering the sources.
source_table : `~astropy.table.Table`
Table of parameters for the sources. Each row of the table
corresponds to a source whose model parameters are defined by
the column names, which must match the model parameter names.
Column names that do not match model parameters will be ignored.
Model parameters not defined in the table will be set to the
``model`` default value.
oversample : float, optional
The sampling factor used to discretize the models on a pixel
grid. If the value is 1.0 (the default), then the models will
be discretized by taking the value at the center of the pixel
bin. Note that this method will not preserve the total flux of
very small sources. Otherwise, the models will be discretized
by taking the average over an oversampled grid. The pixels will
be oversampled by the ``oversample`` factor.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing model sources.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Examples
--------
.. plot::
:include-source:
from collections import OrderedDict
from astropy.modeling.models import Moffat2D
from photutils.datasets import (make_random_models_table,
make_model_sources_image)
model = Moffat2D()
n_sources = 10
shape = (100, 100)
param_ranges = [('amplitude', [100, 200]),
('x_0', [0, shape[1]]),
('y_0', [0, shape[0]]),
('gamma', [5, 10]),
('alpha', [1, 2])]
param_ranges = OrderedDict(param_ranges)
sources = make_random_models_table(n_sources, param_ranges,
random_state=12345)
data = make_model_sources_image(shape, model, sources)
plt.imshow(data)
"""
image = np.zeros(shape, dtype=np.float64)
y, x = np.indices(shape)
params_to_set = []
for param in source_table.colnames:
if param in model.param_names:
params_to_set.append(param)
# Save the initial parameter values so we can set them back when
# done with the loop. It's best not to copy a model, because some
# models (e.g. PSF models) may have substantial amounts of data in
# them.
init_params = {param: getattr(model, param) for param in params_to_set}
try:
for i, source in enumerate(source_table):
for param in params_to_set:
setattr(model, param, source[param])
if oversample == 1:
image += model(x, y)
else:
image += discretize_model(model, (0, shape[1]),
(0, shape[0]), mode='oversample',
factor=oversample)
finally:
for param, value in init_params.items():
setattr(model, param, value)
return image | python | def make_model_sources_image(shape, model, source_table, oversample=1):
"""
Make an image containing sources generated from a user-specified
model.
Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
model : 2D astropy.modeling.models object
The model to be used for rendering the sources.
source_table : `~astropy.table.Table`
Table of parameters for the sources. Each row of the table
corresponds to a source whose model parameters are defined by
the column names, which must match the model parameter names.
Column names that do not match model parameters will be ignored.
Model parameters not defined in the table will be set to the
``model`` default value.
oversample : float, optional
The sampling factor used to discretize the models on a pixel
grid. If the value is 1.0 (the default), then the models will
be discretized by taking the value at the center of the pixel
bin. Note that this method will not preserve the total flux of
very small sources. Otherwise, the models will be discretized
by taking the average over an oversampled grid. The pixels will
be oversampled by the ``oversample`` factor.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing model sources.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Examples
--------
.. plot::
:include-source:
from collections import OrderedDict
from astropy.modeling.models import Moffat2D
from photutils.datasets import (make_random_models_table,
make_model_sources_image)
model = Moffat2D()
n_sources = 10
shape = (100, 100)
param_ranges = [('amplitude', [100, 200]),
('x_0', [0, shape[1]]),
('y_0', [0, shape[0]]),
('gamma', [5, 10]),
('alpha', [1, 2])]
param_ranges = OrderedDict(param_ranges)
sources = make_random_models_table(n_sources, param_ranges,
random_state=12345)
data = make_model_sources_image(shape, model, sources)
plt.imshow(data)
"""
image = np.zeros(shape, dtype=np.float64)
y, x = np.indices(shape)
params_to_set = []
for param in source_table.colnames:
if param in model.param_names:
params_to_set.append(param)
# Save the initial parameter values so we can set them back when
# done with the loop. It's best not to copy a model, because some
# models (e.g. PSF models) may have substantial amounts of data in
# them.
init_params = {param: getattr(model, param) for param in params_to_set}
try:
for i, source in enumerate(source_table):
for param in params_to_set:
setattr(model, param, source[param])
if oversample == 1:
image += model(x, y)
else:
image += discretize_model(model, (0, shape[1]),
(0, shape[0]), mode='oversample',
factor=oversample)
finally:
for param, value in init_params.items():
setattr(model, param, value)
return image | [
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Parameters
----------
shape : 2-tuple of int
The shape of the output 2D image.
model : 2D astropy.modeling.models object
The model to be used for rendering the sources.
source_table : `~astropy.table.Table`
Table of parameters for the sources. Each row of the table
corresponds to a source whose model parameters are defined by
the column names, which must match the model parameter names.
Column names that do not match model parameters will be ignored.
Model parameters not defined in the table will be set to the
``model`` default value.
oversample : float, optional
The sampling factor used to discretize the models on a pixel
grid. If the value is 1.0 (the default), then the models will
be discretized by taking the value at the center of the pixel
bin. Note that this method will not preserve the total flux of
very small sources. Otherwise, the models will be discretized
by taking the average over an oversampled grid. The pixels will
be oversampled by the ``oversample`` factor.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing model sources.
See Also
--------
make_random_models_table, make_gaussian_sources_image
Examples
--------
.. plot::
:include-source:
from collections import OrderedDict
from astropy.modeling.models import Moffat2D
from photutils.datasets import (make_random_models_table,
make_model_sources_image)
model = Moffat2D()
n_sources = 10
shape = (100, 100)
param_ranges = [('amplitude', [100, 200]),
('x_0', [0, shape[1]]),
('y_0', [0, shape[0]]),
('gamma', [5, 10]),
('alpha', [1, 2])]
param_ranges = OrderedDict(param_ranges)
sources = make_random_models_table(n_sources, param_ranges,
random_state=12345)
data = make_model_sources_image(shape, model, sources)
plt.imshow(data) | [
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astropy/photutils | photutils/datasets/make.py | make_4gaussians_image | def make_4gaussians_image(noise=True):
"""
Make an example image containing four 2D Gaussians plus a constant
background.
The background has a mean of 5.
If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 5 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
`True`).
Returns
-------
image : 2D `~numpy.ndarray`
Image containing four 2D Gaussian sources.
See Also
--------
make_100gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_4gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest')
"""
table = Table()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.array([145., 20., 0., 60.]) * np.pi / 180.
shape = (100, 200)
data = make_gaussian_sources_image(shape, table) + 5.
if noise:
data += make_noise_image(shape, type='gaussian', mean=0.,
stddev=5., random_state=12345)
return data | python | def make_4gaussians_image(noise=True):
"""
Make an example image containing four 2D Gaussians plus a constant
background.
The background has a mean of 5.
If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 5 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
`True`).
Returns
-------
image : 2D `~numpy.ndarray`
Image containing four 2D Gaussian sources.
See Also
--------
make_100gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_4gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest')
"""
table = Table()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.array([145., 20., 0., 60.]) * np.pi / 180.
shape = (100, 200)
data = make_gaussian_sources_image(shape, table) + 5.
if noise:
data += make_noise_image(shape, type='gaussian', mean=0.,
stddev=5., random_state=12345)
return data | [
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If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 5 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
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Returns
-------
image : 2D `~numpy.ndarray`
Image containing four 2D Gaussian sources.
See Also
--------
make_100gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_4gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest') | [
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astropy/photutils | photutils/datasets/make.py | make_100gaussians_image | def make_100gaussians_image(noise=True):
"""
Make an example image containing 100 2D Gaussians plus a constant
background.
The background has a mean of 5.
If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 2 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
`True`).
Returns
-------
image : 2D `~numpy.ndarray`
Image containing 100 2D Gaussian sources.
See Also
--------
make_4gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_100gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest')
"""
n_sources = 100
flux_range = [500, 1000]
xmean_range = [0, 500]
ymean_range = [0, 300]
xstddev_range = [1, 5]
ystddev_range = [1, 5]
params = OrderedDict([('flux', flux_range),
('x_mean', xmean_range),
('y_mean', ymean_range),
('x_stddev', xstddev_range),
('y_stddev', ystddev_range),
('theta', [0, 2*np.pi])])
sources = make_random_gaussians_table(n_sources, params,
random_state=12345)
shape = (300, 500)
data = make_gaussian_sources_image(shape, sources) + 5.
if noise:
data += make_noise_image(shape, type='gaussian', mean=0.,
stddev=2., random_state=12345)
return data | python | def make_100gaussians_image(noise=True):
"""
Make an example image containing 100 2D Gaussians plus a constant
background.
The background has a mean of 5.
If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 2 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
`True`).
Returns
-------
image : 2D `~numpy.ndarray`
Image containing 100 2D Gaussian sources.
See Also
--------
make_4gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_100gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest')
"""
n_sources = 100
flux_range = [500, 1000]
xmean_range = [0, 500]
ymean_range = [0, 300]
xstddev_range = [1, 5]
ystddev_range = [1, 5]
params = OrderedDict([('flux', flux_range),
('x_mean', xmean_range),
('y_mean', ymean_range),
('x_stddev', xstddev_range),
('y_stddev', ystddev_range),
('theta', [0, 2*np.pi])])
sources = make_random_gaussians_table(n_sources, params,
random_state=12345)
shape = (300, 500)
data = make_gaussian_sources_image(shape, sources) + 5.
if noise:
data += make_noise_image(shape, type='gaussian', mean=0.,
stddev=2., random_state=12345)
return data | [
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The background has a mean of 5.
If ``noise`` is `True`, then Gaussian noise with a mean of 0 and a
standard deviation of 2 is added to the output image.
Parameters
----------
noise : bool, optional
Whether to include noise in the output image (default is
`True`).
Returns
-------
image : 2D `~numpy.ndarray`
Image containing 100 2D Gaussian sources.
See Also
--------
make_4gaussians_image
Examples
--------
.. plot::
:include-source:
from photutils import datasets
image = datasets.make_100gaussians_image()
plt.imshow(image, origin='lower', interpolation='nearest') | [
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astropy/photutils | photutils/datasets/make.py | make_wcs | def make_wcs(shape, galactic=False):
"""
Create a simple celestial WCS object in either the ICRS or Galactic
coordinate frame.
Parameters
----------
shape : 2-tuple of int
The shape of the 2D array to be used with the output
`~astropy.wcs.WCS` object.
galactic : bool, optional
If `True`, then the output WCS will be in the Galactic
coordinate frame. If `False` (default), then the output WCS
will be in the ICRS coordinate frame.
Returns
-------
wcs : `~astropy.wcs.WCS` object
The world coordinate system (WCS) transformation.
See Also
--------
make_imagehdu
Examples
--------
>>> from photutils.datasets import make_wcs
>>> shape = (100, 100)
>>> wcs = make_wcs(shape)
>>> print(wcs.wcs.crpix) # doctest: +FLOAT_CMP
[50. 50.]
>>> print(wcs.wcs.crval) # doctest: +FLOAT_CMP
[197.8925 -1.36555556]
"""
wcs = WCS(naxis=2)
rho = np.pi / 3.
scale = 0.1 / 3600.
if astropy_version < '3.1':
wcs._naxis1 = shape[1] # nx
wcs._naxis2 = shape[0] # ny
else:
wcs.pixel_shape = shape
wcs.wcs.crpix = [shape[1] / 2, shape[0] / 2] # 1-indexed (x, y)
wcs.wcs.crval = [197.8925, -1.36555556]
wcs.wcs.cunit = ['deg', 'deg']
wcs.wcs.cd = [[-scale * np.cos(rho), scale * np.sin(rho)],
[scale * np.sin(rho), scale * np.cos(rho)]]
if not galactic:
wcs.wcs.radesys = 'ICRS'
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
else:
wcs.wcs.ctype = ['GLON-CAR', 'GLAT-CAR']
return wcs | python | def make_wcs(shape, galactic=False):
"""
Create a simple celestial WCS object in either the ICRS or Galactic
coordinate frame.
Parameters
----------
shape : 2-tuple of int
The shape of the 2D array to be used with the output
`~astropy.wcs.WCS` object.
galactic : bool, optional
If `True`, then the output WCS will be in the Galactic
coordinate frame. If `False` (default), then the output WCS
will be in the ICRS coordinate frame.
Returns
-------
wcs : `~astropy.wcs.WCS` object
The world coordinate system (WCS) transformation.
See Also
--------
make_imagehdu
Examples
--------
>>> from photutils.datasets import make_wcs
>>> shape = (100, 100)
>>> wcs = make_wcs(shape)
>>> print(wcs.wcs.crpix) # doctest: +FLOAT_CMP
[50. 50.]
>>> print(wcs.wcs.crval) # doctest: +FLOAT_CMP
[197.8925 -1.36555556]
"""
wcs = WCS(naxis=2)
rho = np.pi / 3.
scale = 0.1 / 3600.
if astropy_version < '3.1':
wcs._naxis1 = shape[1] # nx
wcs._naxis2 = shape[0] # ny
else:
wcs.pixel_shape = shape
wcs.wcs.crpix = [shape[1] / 2, shape[0] / 2] # 1-indexed (x, y)
wcs.wcs.crval = [197.8925, -1.36555556]
wcs.wcs.cunit = ['deg', 'deg']
wcs.wcs.cd = [[-scale * np.cos(rho), scale * np.sin(rho)],
[scale * np.sin(rho), scale * np.cos(rho)]]
if not galactic:
wcs.wcs.radesys = 'ICRS'
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
else:
wcs.wcs.ctype = ['GLON-CAR', 'GLAT-CAR']
return wcs | [
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----------
shape : 2-tuple of int
The shape of the 2D array to be used with the output
`~astropy.wcs.WCS` object.
galactic : bool, optional
If `True`, then the output WCS will be in the Galactic
coordinate frame. If `False` (default), then the output WCS
will be in the ICRS coordinate frame.
Returns
-------
wcs : `~astropy.wcs.WCS` object
The world coordinate system (WCS) transformation.
See Also
--------
make_imagehdu
Examples
--------
>>> from photutils.datasets import make_wcs
>>> shape = (100, 100)
>>> wcs = make_wcs(shape)
>>> print(wcs.wcs.crpix) # doctest: +FLOAT_CMP
[50. 50.]
>>> print(wcs.wcs.crval) # doctest: +FLOAT_CMP
[197.8925 -1.36555556] | [
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astropy/photutils | photutils/datasets/make.py | make_imagehdu | def make_imagehdu(data, wcs=None):
"""
Create a FITS `~astropy.io.fits.ImageHDU` containing the input 2D
image.
Parameters
----------
data : 2D array-like
The input 2D data.
wcs : `~astropy.wcs.WCS`, optional
The world coordinate system (WCS) transformation to include in
the output FITS header.
Returns
-------
image_hdu : `~astropy.io.fits.ImageHDU`
The FITS `~astropy.io.fits.ImageHDU`.
See Also
--------
make_wcs
Examples
--------
>>> from photutils.datasets import make_imagehdu, make_wcs
>>> shape = (100, 100)
>>> data = np.ones(shape)
>>> wcs = make_wcs(shape)
>>> hdu = make_imagehdu(data, wcs=wcs)
>>> print(hdu.data.shape)
(100, 100)
"""
data = np.asanyarray(data)
if data.ndim != 2:
raise ValueError('data must be a 2D array')
if wcs is not None:
header = wcs.to_header()
else:
header = None
return fits.ImageHDU(data, header=header) | python | def make_imagehdu(data, wcs=None):
"""
Create a FITS `~astropy.io.fits.ImageHDU` containing the input 2D
image.
Parameters
----------
data : 2D array-like
The input 2D data.
wcs : `~astropy.wcs.WCS`, optional
The world coordinate system (WCS) transformation to include in
the output FITS header.
Returns
-------
image_hdu : `~astropy.io.fits.ImageHDU`
The FITS `~astropy.io.fits.ImageHDU`.
See Also
--------
make_wcs
Examples
--------
>>> from photutils.datasets import make_imagehdu, make_wcs
>>> shape = (100, 100)
>>> data = np.ones(shape)
>>> wcs = make_wcs(shape)
>>> hdu = make_imagehdu(data, wcs=wcs)
>>> print(hdu.data.shape)
(100, 100)
"""
data = np.asanyarray(data)
if data.ndim != 2:
raise ValueError('data must be a 2D array')
if wcs is not None:
header = wcs.to_header()
else:
header = None
return fits.ImageHDU(data, header=header) | [
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The input 2D data.
wcs : `~astropy.wcs.WCS`, optional
The world coordinate system (WCS) transformation to include in
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Returns
-------
image_hdu : `~astropy.io.fits.ImageHDU`
The FITS `~astropy.io.fits.ImageHDU`.
See Also
--------
make_wcs
Examples
--------
>>> from photutils.datasets import make_imagehdu, make_wcs
>>> shape = (100, 100)
>>> data = np.ones(shape)
>>> wcs = make_wcs(shape)
>>> hdu = make_imagehdu(data, wcs=wcs)
>>> print(hdu.data.shape)
(100, 100) | [
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astropy/photutils | photutils/centroids/core.py | centroid_com | def centroid_com(data, mask=None):
"""
Calculate the centroid of an n-dimensional array as its "center of
mass" determined from moments.
Invalid values (e.g. NaNs or infs) in the ``data`` array are
automatically masked.
Parameters
----------
data : array_like
The input n-dimensional array.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
centroid : `~numpy.ndarray`
The coordinates of the centroid in pixel order (e.g. ``(x, y)``
or ``(x, y, z)``), not numpy axis order.
"""
data = data.astype(np.float)
if mask is not None and mask is not np.ma.nomask:
mask = np.asarray(mask, dtype=bool)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data[mask] = 0.
badidx = ~np.isfinite(data)
if np.any(badidx):
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
data[badidx] = 0.
total = np.sum(data)
indices = np.ogrid[[slice(0, i) for i in data.shape]]
# note the output array is reversed to give (x, y) order
return np.array([np.sum(indices[axis] * data) / total
for axis in range(data.ndim)])[::-1] | python | def centroid_com(data, mask=None):
"""
Calculate the centroid of an n-dimensional array as its "center of
mass" determined from moments.
Invalid values (e.g. NaNs or infs) in the ``data`` array are
automatically masked.
Parameters
----------
data : array_like
The input n-dimensional array.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
centroid : `~numpy.ndarray`
The coordinates of the centroid in pixel order (e.g. ``(x, y)``
or ``(x, y, z)``), not numpy axis order.
"""
data = data.astype(np.float)
if mask is not None and mask is not np.ma.nomask:
mask = np.asarray(mask, dtype=bool)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data[mask] = 0.
badidx = ~np.isfinite(data)
if np.any(badidx):
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
data[badidx] = 0.
total = np.sum(data)
indices = np.ogrid[[slice(0, i) for i in data.shape]]
# note the output array is reversed to give (x, y) order
return np.array([np.sum(indices[axis] * data) / total
for axis in range(data.ndim)])[::-1] | [
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A boolean mask, with the same shape as ``data``, where a `True`
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Returns
-------
centroid : `~numpy.ndarray`
The coordinates of the centroid in pixel order (e.g. ``(x, y)``
or ``(x, y, z)``), not numpy axis order. | [
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astropy/photutils | photutils/centroids/core.py | gaussian1d_moments | def gaussian1d_moments(data, mask=None):
"""
Estimate 1D Gaussian parameters from the moments of 1D data.
This function can be useful for providing initial parameter values
when fitting a 1D Gaussian to the ``data``.
Parameters
----------
data : array_like (1D)
The 1D array.
mask : array_like (1D bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
amplitude, mean, stddev : float
The estimated parameters of a 1D Gaussian.
"""
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
else:
data = np.ma.array(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
data.fill_value = 0.
data = data.filled()
x = np.arange(data.size)
x_mean = np.sum(x * data) / np.sum(data)
x_stddev = np.sqrt(abs(np.sum(data * (x - x_mean)**2) / np.sum(data)))
amplitude = np.ptp(data)
return amplitude, x_mean, x_stddev | python | def gaussian1d_moments(data, mask=None):
"""
Estimate 1D Gaussian parameters from the moments of 1D data.
This function can be useful for providing initial parameter values
when fitting a 1D Gaussian to the ``data``.
Parameters
----------
data : array_like (1D)
The 1D array.
mask : array_like (1D bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
amplitude, mean, stddev : float
The estimated parameters of a 1D Gaussian.
"""
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
else:
data = np.ma.array(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
data.fill_value = 0.
data = data.filled()
x = np.arange(data.size)
x_mean = np.sum(x * data) / np.sum(data)
x_stddev = np.sqrt(abs(np.sum(data * (x - x_mean)**2) / np.sum(data)))
amplitude = np.ptp(data)
return amplitude, x_mean, x_stddev | [
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Parameters
----------
data : array_like (1D)
The 1D array.
mask : array_like (1D bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
amplitude, mean, stddev : float
The estimated parameters of a 1D Gaussian. | [
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astropy/photutils | photutils/centroids/core.py | fit_2dgaussian | def fit_2dgaussian(data, error=None, mask=None):
"""
Fit a 2D Gaussian plus a constant to a 2D image.
Invalid values (e.g. NaNs or infs) in the ``data`` or ``error``
arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
``data`` and ``error`` arrays.
Parameters
----------
data : array_like
The 2D array of the image.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
result : A `GaussianConst2D` model instance.
The best-fitting Gaussian 2D model.
"""
from ..morphology import data_properties # prevent circular imports
data = np.ma.asanyarray(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
if error is not None:
error = np.ma.masked_invalid(error)
if data.shape != error.shape:
raise ValueError('data and error must have the same shape.')
data.mask |= error.mask
weights = 1.0 / error.clip(min=1.e-30)
else:
weights = np.ones(data.shape)
if np.ma.count(data) < 7:
raise ValueError('Input data must have a least 7 unmasked values to '
'fit a 2D Gaussian plus a constant.')
# assign zero weight to masked pixels
if data.mask is not np.ma.nomask:
weights[data.mask] = 0.
mask = data.mask
data.fill_value = 0.0
data = data.filled()
# Subtract the minimum of the data as a crude background estimate.
# This will also make the data values positive, preventing issues with
# the moment estimation in data_properties (moments from negative data
# values can yield undefined Gaussian parameters, e.g. x/y_stddev).
props = data_properties(data - np.min(data), mask=mask)
init_const = 0. # subtracted data minimum above
init_amplitude = np.ptp(data)
g_init = GaussianConst2D(constant=init_const, amplitude=init_amplitude,
x_mean=props.xcentroid.value,
y_mean=props.ycentroid.value,
x_stddev=props.semimajor_axis_sigma.value,
y_stddev=props.semiminor_axis_sigma.value,
theta=props.orientation.value)
fitter = LevMarLSQFitter()
y, x = np.indices(data.shape)
gfit = fitter(g_init, x, y, data, weights=weights)
return gfit | python | def fit_2dgaussian(data, error=None, mask=None):
"""
Fit a 2D Gaussian plus a constant to a 2D image.
Invalid values (e.g. NaNs or infs) in the ``data`` or ``error``
arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
``data`` and ``error`` arrays.
Parameters
----------
data : array_like
The 2D array of the image.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
result : A `GaussianConst2D` model instance.
The best-fitting Gaussian 2D model.
"""
from ..morphology import data_properties # prevent circular imports
data = np.ma.asanyarray(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
if error is not None:
error = np.ma.masked_invalid(error)
if data.shape != error.shape:
raise ValueError('data and error must have the same shape.')
data.mask |= error.mask
weights = 1.0 / error.clip(min=1.e-30)
else:
weights = np.ones(data.shape)
if np.ma.count(data) < 7:
raise ValueError('Input data must have a least 7 unmasked values to '
'fit a 2D Gaussian plus a constant.')
# assign zero weight to masked pixels
if data.mask is not np.ma.nomask:
weights[data.mask] = 0.
mask = data.mask
data.fill_value = 0.0
data = data.filled()
# Subtract the minimum of the data as a crude background estimate.
# This will also make the data values positive, preventing issues with
# the moment estimation in data_properties (moments from negative data
# values can yield undefined Gaussian parameters, e.g. x/y_stddev).
props = data_properties(data - np.min(data), mask=mask)
init_const = 0. # subtracted data minimum above
init_amplitude = np.ptp(data)
g_init = GaussianConst2D(constant=init_const, amplitude=init_amplitude,
x_mean=props.xcentroid.value,
y_mean=props.ycentroid.value,
x_stddev=props.semimajor_axis_sigma.value,
y_stddev=props.semiminor_axis_sigma.value,
theta=props.orientation.value)
fitter = LevMarLSQFitter()
y, x = np.indices(data.shape)
gfit = fitter(g_init, x, y, data, weights=weights)
return gfit | [
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arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
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Parameters
----------
data : array_like
The 2D array of the image.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
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Returns
-------
result : A `GaussianConst2D` model instance.
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astropy/photutils | photutils/centroids/core.py | centroid_1dg | def centroid_1dg(data, error=None, mask=None):
"""
Calculate the centroid of a 2D array by fitting 1D Gaussians to the
marginal ``x`` and ``y`` distributions of the array.
Invalid values (e.g. NaNs or infs) in the ``data`` or ``error``
arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
``data`` and ``error`` arrays.
Parameters
----------
data : array_like
The 2D data array.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
centroid : `~numpy.ndarray`
The ``x, y`` coordinates of the centroid.
"""
data = np.ma.asanyarray(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
if error is not None:
error = np.ma.masked_invalid(error)
if data.shape != error.shape:
raise ValueError('data and error must have the same shape.')
data.mask |= error.mask
error.mask = data.mask
xy_error = np.array([np.sqrt(np.ma.sum(error**2, axis=i))
for i in [0, 1]])
xy_weights = [(1.0 / xy_error[i].clip(min=1.e-30)) for i in [0, 1]]
else:
xy_weights = [np.ones(data.shape[i]) for i in [1, 0]]
# assign zero weight to masked pixels
if data.mask is not np.ma.nomask:
bad_idx = [np.all(data.mask, axis=i) for i in [0, 1]]
for i in [0, 1]:
xy_weights[i][bad_idx[i]] = 0.
xy_data = np.array([np.ma.sum(data, axis=i) for i in [0, 1]])
constant_init = np.ma.min(data)
centroid = []
for (data_i, weights_i) in zip(xy_data, xy_weights):
params_init = gaussian1d_moments(data_i)
g_init = Const1D(constant_init) + Gaussian1D(*params_init)
fitter = LevMarLSQFitter()
x = np.arange(data_i.size)
g_fit = fitter(g_init, x, data_i, weights=weights_i)
centroid.append(g_fit.mean_1.value)
return np.array(centroid) | python | def centroid_1dg(data, error=None, mask=None):
"""
Calculate the centroid of a 2D array by fitting 1D Gaussians to the
marginal ``x`` and ``y`` distributions of the array.
Invalid values (e.g. NaNs or infs) in the ``data`` or ``error``
arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
``data`` and ``error`` arrays.
Parameters
----------
data : array_like
The 2D data array.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
centroid : `~numpy.ndarray`
The ``x, y`` coordinates of the centroid.
"""
data = np.ma.asanyarray(data)
if mask is not None and mask is not np.ma.nomask:
mask = np.asanyarray(mask)
if data.shape != mask.shape:
raise ValueError('data and mask must have the same shape.')
data.mask |= mask
if np.any(~np.isfinite(data)):
data = np.ma.masked_invalid(data)
warnings.warn('Input data contains input values (e.g. NaNs or infs), '
'which were automatically masked.', AstropyUserWarning)
if error is not None:
error = np.ma.masked_invalid(error)
if data.shape != error.shape:
raise ValueError('data and error must have the same shape.')
data.mask |= error.mask
error.mask = data.mask
xy_error = np.array([np.sqrt(np.ma.sum(error**2, axis=i))
for i in [0, 1]])
xy_weights = [(1.0 / xy_error[i].clip(min=1.e-30)) for i in [0, 1]]
else:
xy_weights = [np.ones(data.shape[i]) for i in [1, 0]]
# assign zero weight to masked pixels
if data.mask is not np.ma.nomask:
bad_idx = [np.all(data.mask, axis=i) for i in [0, 1]]
for i in [0, 1]:
xy_weights[i][bad_idx[i]] = 0.
xy_data = np.array([np.ma.sum(data, axis=i) for i in [0, 1]])
constant_init = np.ma.min(data)
centroid = []
for (data_i, weights_i) in zip(xy_data, xy_weights):
params_init = gaussian1d_moments(data_i)
g_init = Const1D(constant_init) + Gaussian1D(*params_init)
fitter = LevMarLSQFitter()
x = np.arange(data_i.size)
g_fit = fitter(g_init, x, data_i, weights=weights_i)
centroid.append(g_fit.mean_1.value)
return np.array(centroid) | [
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] | Calculate the centroid of a 2D array by fitting 1D Gaussians to the
marginal ``x`` and ``y`` distributions of the array.
Invalid values (e.g. NaNs or infs) in the ``data`` or ``error``
arrays are automatically masked. The mask for invalid values
represents the combination of the invalid-value masks for the
``data`` and ``error`` arrays.
Parameters
----------
data : array_like
The 2D data array.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
mask : array_like (bool), optional
A boolean mask, with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Returns
-------
centroid : `~numpy.ndarray`
The ``x, y`` coordinates of the centroid. | [
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astropy/photutils | photutils/centroids/core.py | centroid_sources | def centroid_sources(data, xpos, ypos, box_size=11, footprint=None,
error=None, mask=None, centroid_func=centroid_com):
"""
Calculate the centroid of sources at the defined positions.
A cutout image centered on each input position will be used to
calculate the centroid position. The cutout image is defined either
using the ``box_size`` or ``footprint`` keyword. The ``footprint``
keyword can be used to create a non-rectangular cutout image.
Parameters
----------
data : array_like
The 2D array of the image.
xpos, ypos : float or array-like of float
The initial ``x`` and ``y`` pixel position(s) of the center
position. A cutout image centered on this position be used to
calculate the centroid.
box_size : int or array-like of int, optional
The size of the cutout image along each axis. If ``box_size``
is a number, then a square cutout of ``box_size`` will be
created. If ``box_size`` has two elements, they should be in
``(ny, nx)`` order.
footprint : `~numpy.ndarray` of bools, optional
A 2D boolean array where `True` values describe the local
footprint region to cutout. ``footprint`` can be used to create
a non-rectangular cutout image, in which case the input ``xpos``
and ``ypos`` represent the center of the minimal bounding box
for the input ``footprint``. ``box_size=(n, m)`` is equivalent
to ``footprint=np.ones((n, m))``. Either ``box_size`` or
``footprint`` must be defined. If they are both defined, then
``footprint`` overrides ``box_size``.
mask : array_like, bool, optional
A 2D boolean array with the same shape as ``data``, where a
`True` value indicates the corresponding element of ``data`` is
masked.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
``error`` must have the same shape as ``data``. ``error`` will
be used only if supported by the input ``centroid_func``.
centroid_func : callable, optional
A callable object (e.g. function or class) that is used to
calculate the centroid of a 2D array. The ``centroid_func``
must accept a 2D `~numpy.ndarray`, have a ``mask`` keyword and
optionally an ``error`` keyword. The callable object must
return a tuple of two 1D `~numpy.ndarray`\\s, representing the x
and y centroids. The default is
`~photutils.centroids.centroid_com`.
Returns
-------
xcentroid, ycentroid : `~numpy.ndarray`
The ``x`` and ``y`` pixel position(s) of the centroids.
"""
xpos = np.atleast_1d(xpos)
ypos = np.atleast_1d(ypos)
if xpos.ndim != 1:
raise ValueError('xpos must be a 1D array.')
if ypos.ndim != 1:
raise ValueError('ypos must be a 1D array.')
if footprint is None:
if box_size is None:
raise ValueError('box_size or footprint must be defined.')
else:
box_size = np.atleast_1d(box_size)
if len(box_size) == 1:
box_size = np.repeat(box_size, 2)
if len(box_size) != 2:
raise ValueError('box_size must have 1 or 2 elements.')
footprint = np.ones(box_size, dtype=bool)
else:
footprint = np.asanyarray(footprint, dtype=bool)
if footprint.ndim != 2:
raise ValueError('footprint must be a 2D array.')
use_error = False
spec = inspect.getfullargspec(centroid_func)
if 'mask' not in spec.args:
raise ValueError('The input "centroid_func" must have a "mask" '
'keyword.')
if 'error' in spec.args:
use_error = True
xcentroids = []
ycentroids = []
for xp, yp in zip(xpos, ypos):
slices_large, slices_small = overlap_slices(data.shape,
footprint.shape, (yp, xp))
data_cutout = data[slices_large]
mask_cutout = None
if mask is not None:
mask_cutout = mask[slices_large]
footprint_mask = ~footprint
# trim footprint mask if partial overlap on the data
footprint_mask = footprint_mask[slices_small]
if mask_cutout is None:
mask_cutout = footprint_mask
else:
# combine the input mask and footprint mask
mask_cutout = np.logical_or(mask_cutout, footprint_mask)
if error is not None and use_error:
error_cutout = error[slices_large]
xcen, ycen = centroid_func(data_cutout, mask=mask_cutout,
error=error_cutout)
else:
xcen, ycen = centroid_func(data_cutout, mask=mask_cutout)
xcentroids.append(xcen + slices_large[1].start)
ycentroids.append(ycen + slices_large[0].start)
return np.array(xcentroids), np.array(ycentroids) | python | def centroid_sources(data, xpos, ypos, box_size=11, footprint=None,
error=None, mask=None, centroid_func=centroid_com):
"""
Calculate the centroid of sources at the defined positions.
A cutout image centered on each input position will be used to
calculate the centroid position. The cutout image is defined either
using the ``box_size`` or ``footprint`` keyword. The ``footprint``
keyword can be used to create a non-rectangular cutout image.
Parameters
----------
data : array_like
The 2D array of the image.
xpos, ypos : float or array-like of float
The initial ``x`` and ``y`` pixel position(s) of the center
position. A cutout image centered on this position be used to
calculate the centroid.
box_size : int or array-like of int, optional
The size of the cutout image along each axis. If ``box_size``
is a number, then a square cutout of ``box_size`` will be
created. If ``box_size`` has two elements, they should be in
``(ny, nx)`` order.
footprint : `~numpy.ndarray` of bools, optional
A 2D boolean array where `True` values describe the local
footprint region to cutout. ``footprint`` can be used to create
a non-rectangular cutout image, in which case the input ``xpos``
and ``ypos`` represent the center of the minimal bounding box
for the input ``footprint``. ``box_size=(n, m)`` is equivalent
to ``footprint=np.ones((n, m))``. Either ``box_size`` or
``footprint`` must be defined. If they are both defined, then
``footprint`` overrides ``box_size``.
mask : array_like, bool, optional
A 2D boolean array with the same shape as ``data``, where a
`True` value indicates the corresponding element of ``data`` is
masked.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
``error`` must have the same shape as ``data``. ``error`` will
be used only if supported by the input ``centroid_func``.
centroid_func : callable, optional
A callable object (e.g. function or class) that is used to
calculate the centroid of a 2D array. The ``centroid_func``
must accept a 2D `~numpy.ndarray`, have a ``mask`` keyword and
optionally an ``error`` keyword. The callable object must
return a tuple of two 1D `~numpy.ndarray`\\s, representing the x
and y centroids. The default is
`~photutils.centroids.centroid_com`.
Returns
-------
xcentroid, ycentroid : `~numpy.ndarray`
The ``x`` and ``y`` pixel position(s) of the centroids.
"""
xpos = np.atleast_1d(xpos)
ypos = np.atleast_1d(ypos)
if xpos.ndim != 1:
raise ValueError('xpos must be a 1D array.')
if ypos.ndim != 1:
raise ValueError('ypos must be a 1D array.')
if footprint is None:
if box_size is None:
raise ValueError('box_size or footprint must be defined.')
else:
box_size = np.atleast_1d(box_size)
if len(box_size) == 1:
box_size = np.repeat(box_size, 2)
if len(box_size) != 2:
raise ValueError('box_size must have 1 or 2 elements.')
footprint = np.ones(box_size, dtype=bool)
else:
footprint = np.asanyarray(footprint, dtype=bool)
if footprint.ndim != 2:
raise ValueError('footprint must be a 2D array.')
use_error = False
spec = inspect.getfullargspec(centroid_func)
if 'mask' not in spec.args:
raise ValueError('The input "centroid_func" must have a "mask" '
'keyword.')
if 'error' in spec.args:
use_error = True
xcentroids = []
ycentroids = []
for xp, yp in zip(xpos, ypos):
slices_large, slices_small = overlap_slices(data.shape,
footprint.shape, (yp, xp))
data_cutout = data[slices_large]
mask_cutout = None
if mask is not None:
mask_cutout = mask[slices_large]
footprint_mask = ~footprint
# trim footprint mask if partial overlap on the data
footprint_mask = footprint_mask[slices_small]
if mask_cutout is None:
mask_cutout = footprint_mask
else:
# combine the input mask and footprint mask
mask_cutout = np.logical_or(mask_cutout, footprint_mask)
if error is not None and use_error:
error_cutout = error[slices_large]
xcen, ycen = centroid_func(data_cutout, mask=mask_cutout,
error=error_cutout)
else:
xcen, ycen = centroid_func(data_cutout, mask=mask_cutout)
xcentroids.append(xcen + slices_large[1].start)
ycentroids.append(ycen + slices_large[0].start)
return np.array(xcentroids), np.array(ycentroids) | [
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A cutout image centered on each input position will be used to
calculate the centroid position. The cutout image is defined either
using the ``box_size`` or ``footprint`` keyword. The ``footprint``
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Parameters
----------
data : array_like
The 2D array of the image.
xpos, ypos : float or array-like of float
The initial ``x`` and ``y`` pixel position(s) of the center
position. A cutout image centered on this position be used to
calculate the centroid.
box_size : int or array-like of int, optional
The size of the cutout image along each axis. If ``box_size``
is a number, then a square cutout of ``box_size`` will be
created. If ``box_size`` has two elements, they should be in
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footprint : `~numpy.ndarray` of bools, optional
A 2D boolean array where `True` values describe the local
footprint region to cutout. ``footprint`` can be used to create
a non-rectangular cutout image, in which case the input ``xpos``
and ``ypos`` represent the center of the minimal bounding box
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mask : array_like, bool, optional
A 2D boolean array with the same shape as ``data``, where a
`True` value indicates the corresponding element of ``data`` is
masked.
error : array_like, optional
The 2D array of the 1-sigma errors of the input ``data``.
``error`` must have the same shape as ``data``. ``error`` will
be used only if supported by the input ``centroid_func``.
centroid_func : callable, optional
A callable object (e.g. function or class) that is used to
calculate the centroid of a 2D array. The ``centroid_func``
must accept a 2D `~numpy.ndarray`, have a ``mask`` keyword and
optionally an ``error`` keyword. The callable object must
return a tuple of two 1D `~numpy.ndarray`\\s, representing the x
and y centroids. The default is
`~photutils.centroids.centroid_com`.
Returns
-------
xcentroid, ycentroid : `~numpy.ndarray`
The ``x`` and ``y`` pixel position(s) of the centroids. | [
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astropy/photutils | photutils/centroids/core.py | GaussianConst2D.evaluate | def evaluate(x, y, constant, amplitude, x_mean, y_mean, x_stddev,
y_stddev, theta):
"""Two dimensional Gaussian plus constant function."""
model = Const2D(constant)(x, y) + Gaussian2D(amplitude, x_mean,
y_mean, x_stddev,
y_stddev, theta)(x, y)
return model | python | def evaluate(x, y, constant, amplitude, x_mean, y_mean, x_stddev,
y_stddev, theta):
"""Two dimensional Gaussian plus constant function."""
model = Const2D(constant)(x, y) + Gaussian2D(amplitude, x_mean,
y_mean, x_stddev,
y_stddev, theta)(x, y)
return model | [
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astropy/photutils | photutils/psf/sandbox.py | DiscretePRF.evaluate | def evaluate(self, x, y, flux, x_0, y_0):
"""
Discrete PRF model evaluation.
Given a certain position and flux the corresponding image of
the PSF is chosen and scaled to the flux. If x and y are
outside the boundaries of the image, zero will be returned.
Parameters
----------
x : float
x coordinate array in pixel coordinates.
y : float
y coordinate array in pixel coordinates.
flux : float
Model flux.
x_0 : float
x position of the center of the PRF.
y_0 : float
y position of the center of the PRF.
"""
# Convert x and y to index arrays
x = (x - x_0 + 0.5 + self.prf_shape[1] // 2).astype('int')
y = (y - y_0 + 0.5 + self.prf_shape[0] // 2).astype('int')
# Get subpixel indices
y_sub, x_sub = subpixel_indices((y_0, x_0), self.subsampling)
# Out of boundary masks
x_bound = np.logical_or(x < 0, x >= self.prf_shape[1])
y_bound = np.logical_or(y < 0, y >= self.prf_shape[0])
out_of_bounds = np.logical_or(x_bound, y_bound)
# Set out of boundary indices to zero
x[x_bound] = 0
y[y_bound] = 0
result = flux * self._prf_array[int(y_sub), int(x_sub)][y, x]
# Set out of boundary values to zero
result[out_of_bounds] = 0
return result | python | def evaluate(self, x, y, flux, x_0, y_0):
"""
Discrete PRF model evaluation.
Given a certain position and flux the corresponding image of
the PSF is chosen and scaled to the flux. If x and y are
outside the boundaries of the image, zero will be returned.
Parameters
----------
x : float
x coordinate array in pixel coordinates.
y : float
y coordinate array in pixel coordinates.
flux : float
Model flux.
x_0 : float
x position of the center of the PRF.
y_0 : float
y position of the center of the PRF.
"""
# Convert x and y to index arrays
x = (x - x_0 + 0.5 + self.prf_shape[1] // 2).astype('int')
y = (y - y_0 + 0.5 + self.prf_shape[0] // 2).astype('int')
# Get subpixel indices
y_sub, x_sub = subpixel_indices((y_0, x_0), self.subsampling)
# Out of boundary masks
x_bound = np.logical_or(x < 0, x >= self.prf_shape[1])
y_bound = np.logical_or(y < 0, y >= self.prf_shape[0])
out_of_bounds = np.logical_or(x_bound, y_bound)
# Set out of boundary indices to zero
x[x_bound] = 0
y[y_bound] = 0
result = flux * self._prf_array[int(y_sub), int(x_sub)][y, x]
# Set out of boundary values to zero
result[out_of_bounds] = 0
return result | [
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outside the boundaries of the image, zero will be returned.
Parameters
----------
x : float
x coordinate array in pixel coordinates.
y : float
y coordinate array in pixel coordinates.
flux : float
Model flux.
x_0 : float
x position of the center of the PRF.
y_0 : float
y position of the center of the PRF. | [
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astropy/photutils | photutils/psf/sandbox.py | Reproject._reproject | def _reproject(wcs1, wcs2):
"""
Perform the forward transformation of ``wcs1`` followed by the
inverse transformation of ``wcs2``.
Parameters
----------
wcs1, wcs2 : `~astropy.wcs.WCS` or `~gwcs.wcs.WCS`
The WCS objects.
Returns
-------
result : func
Function to compute the transformations. It takes x, y
positions in ``wcs1`` and returns x, y positions in
``wcs2``. The input and output x, y positions are zero
indexed.
"""
import gwcs
forward_origin = []
if isinstance(wcs1, fitswcs.WCS):
forward = wcs1.all_pix2world
forward_origin = [0]
elif isinstance(wcs2, gwcs.wcs.WCS):
forward = wcs1.forward_transform
else:
raise ValueError('wcs1 must be an astropy.wcs.WCS or '
'gwcs.wcs.WCS object.')
inverse_origin = []
if isinstance(wcs2, fitswcs.WCS):
inverse = wcs2.all_world2pix
inverse_origin = [0]
elif isinstance(wcs2, gwcs.wcs.WCS):
inverse = wcs2.forward_transform.inverse
else:
raise ValueError('wcs2 must be an astropy.wcs.WCS or '
'gwcs.wcs.WCS object.')
def _reproject_func(x, y):
forward_args = [x, y] + forward_origin
sky = forward(*forward_args)
inverse_args = sky + inverse_origin
return inverse(*inverse_args)
return _reproject_func | python | def _reproject(wcs1, wcs2):
"""
Perform the forward transformation of ``wcs1`` followed by the
inverse transformation of ``wcs2``.
Parameters
----------
wcs1, wcs2 : `~astropy.wcs.WCS` or `~gwcs.wcs.WCS`
The WCS objects.
Returns
-------
result : func
Function to compute the transformations. It takes x, y
positions in ``wcs1`` and returns x, y positions in
``wcs2``. The input and output x, y positions are zero
indexed.
"""
import gwcs
forward_origin = []
if isinstance(wcs1, fitswcs.WCS):
forward = wcs1.all_pix2world
forward_origin = [0]
elif isinstance(wcs2, gwcs.wcs.WCS):
forward = wcs1.forward_transform
else:
raise ValueError('wcs1 must be an astropy.wcs.WCS or '
'gwcs.wcs.WCS object.')
inverse_origin = []
if isinstance(wcs2, fitswcs.WCS):
inverse = wcs2.all_world2pix
inverse_origin = [0]
elif isinstance(wcs2, gwcs.wcs.WCS):
inverse = wcs2.forward_transform.inverse
else:
raise ValueError('wcs2 must be an astropy.wcs.WCS or '
'gwcs.wcs.WCS object.')
def _reproject_func(x, y):
forward_args = [x, y] + forward_origin
sky = forward(*forward_args)
inverse_args = sky + inverse_origin
return inverse(*inverse_args)
return _reproject_func | [
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The WCS objects.
Returns
-------
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Function to compute the transformations. It takes x, y
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astropy/photutils | photutils/utils/misc.py | get_version_info | def get_version_info():
"""
Return astropy and photutils versions.
Returns
-------
result : str
The astropy and photutils versions.
"""
from astropy import __version__
astropy_version = __version__
from photutils import __version__
photutils_version = __version__
return 'astropy: {0}, photutils: {1}'.format(astropy_version,
photutils_version) | python | def get_version_info():
"""
Return astropy and photutils versions.
Returns
-------
result : str
The astropy and photutils versions.
"""
from astropy import __version__
astropy_version = __version__
from photutils import __version__
photutils_version = __version__
return 'astropy: {0}, photutils: {1}'.format(astropy_version,
photutils_version) | [
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astropy/photutils | photutils/utils/errors.py | calc_total_error | def calc_total_error(data, bkg_error, effective_gain):
"""
Calculate a total error array, combining a background-only error
array with the Poisson noise of sources.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The data array.
bkg_error : array_like or `~astropy.units.Quantity`
The pixel-wise Gaussian 1-sigma background-only errors of the
input ``data``. ``bkg_error`` should include all sources of
"background" error but *exclude* the Poisson error of the
sources. ``bkg_error`` must have the same shape as ``data``.
If ``data`` and ``bkg_error`` are `~astropy.units.Quantity`
objects, then they must have the same units.
effective_gain : float, array-like, or `~astropy.units.Quantity`
Ratio of counts (e.g., electrons or photons) to the units of
``data`` used to calculate the Poisson error of the sources.
Returns
-------
total_error : `~numpy.ndarray` or `~astropy.units.Quantity`
The total error array. If ``data``, ``bkg_error``, and
``effective_gain`` are all `~astropy.units.Quantity` objects,
then ``total_error`` will also be returned as a
`~astropy.units.Quantity` object with the same units as the
input ``data``. Otherwise, a `~numpy.ndarray` will be returned.
Notes
-----
To use units, ``data``, ``bkg_error``, and ``effective_gain`` must
*all* be `~astropy.units.Quantity` objects. ``data`` and
``bkg_error`` must have the same units. A `ValueError` will be
raised if only some of the inputs are `~astropy.units.Quantity`
objects or if the ``data`` and ``bkg_error`` units differ.
The total error array, :math:`\\sigma_{\\mathrm{tot}}` is:
.. math:: \\sigma_{\\mathrm{tot}} = \\sqrt{\\sigma_{\\mathrm{b}}^2 +
\\frac{I}{g}}
where :math:`\\sigma_b`, :math:`I`, and :math:`g` are the background
``bkg_error`` image, ``data`` image, and ``effective_gain``,
respectively.
Pixels where ``data`` (:math:`I_i)` is negative do not contribute
additional Poisson noise to the total error, i.e.
:math:`\\sigma_{\\mathrm{tot}, i} = \\sigma_{\\mathrm{b}, i}`. Note
that this is different from `SExtractor`_, which sums the total
variance in the segment, including pixels where :math:`I_i` is
negative. In such cases, `SExtractor`_ underestimates the total
errors. Also note that SExtractor computes Poisson errors from
background-subtracted data, which also results in an underestimation
of the Poisson noise.
``effective_gain`` can either be a scalar value or a 2D image with
the same shape as the ``data``. A 2D image is useful with mosaic
images that have variable depths (i.e., exposure times) across the
field. For example, one should use an exposure-time map as the
``effective_gain`` for a variable depth mosaic image in count-rate
units.
As an example, if your input ``data`` are in units of ADU, then
``effective_gain`` should be in units of electrons/ADU (or
photons/ADU). If your input ``data`` are in units of electrons/s
then ``effective_gain`` should be the exposure time or an exposure
time map (e.g., for mosaics with non-uniform exposure times).
.. _SExtractor: http://www.astromatic.net/software/sextractor
"""
data = np.asanyarray(data)
bkg_error = np.asanyarray(bkg_error)
inputs = [data, bkg_error, effective_gain]
has_unit = [hasattr(x, 'unit') for x in inputs]
use_units = all(has_unit)
if any(has_unit) and not use_units:
raise ValueError('If any of data, bkg_error, or effective_gain has '
'units, then they all must all have units.')
if use_units:
if data.unit != bkg_error.unit:
raise ValueError('data and bkg_error must have the same units.')
count_units = [u.electron, u.photon]
datagain_unit = data.unit * effective_gain.unit
if datagain_unit not in count_units:
raise u.UnitsError('(data * effective_gain) has units of "{0}", '
'but it must have count units (e.g. '
'u.electron or u.photon).'
.format(datagain_unit))
if not isiterable(effective_gain):
effective_gain = np.zeros(data.shape) + effective_gain
else:
effective_gain = np.asanyarray(effective_gain)
if effective_gain.shape != data.shape:
raise ValueError('If input effective_gain is 2D, then it must '
'have the same shape as the input data.')
if np.any(effective_gain <= 0):
raise ValueError('effective_gain must be strictly positive '
'everywhere.')
# This calculation assumes that data and bkg_error have the same
# units. source_variance is calculated to have units of
# (data.unit)**2 so that it can be added with bkg_error**2 below. The
# final returned error will have units of data.unit. np.maximum is
# used to ensure that negative data values do not contribute to the
# Poisson noise.
if use_units:
unit = data.unit
data = data.value
effective_gain = effective_gain.value
source_variance = np.maximum(data / effective_gain, 0) * unit**2
else:
source_variance = np.maximum(data / effective_gain, 0)
return np.sqrt(bkg_error**2 + source_variance) | python | def calc_total_error(data, bkg_error, effective_gain):
"""
Calculate a total error array, combining a background-only error
array with the Poisson noise of sources.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The data array.
bkg_error : array_like or `~astropy.units.Quantity`
The pixel-wise Gaussian 1-sigma background-only errors of the
input ``data``. ``bkg_error`` should include all sources of
"background" error but *exclude* the Poisson error of the
sources. ``bkg_error`` must have the same shape as ``data``.
If ``data`` and ``bkg_error`` are `~astropy.units.Quantity`
objects, then they must have the same units.
effective_gain : float, array-like, or `~astropy.units.Quantity`
Ratio of counts (e.g., electrons or photons) to the units of
``data`` used to calculate the Poisson error of the sources.
Returns
-------
total_error : `~numpy.ndarray` or `~astropy.units.Quantity`
The total error array. If ``data``, ``bkg_error``, and
``effective_gain`` are all `~astropy.units.Quantity` objects,
then ``total_error`` will also be returned as a
`~astropy.units.Quantity` object with the same units as the
input ``data``. Otherwise, a `~numpy.ndarray` will be returned.
Notes
-----
To use units, ``data``, ``bkg_error``, and ``effective_gain`` must
*all* be `~astropy.units.Quantity` objects. ``data`` and
``bkg_error`` must have the same units. A `ValueError` will be
raised if only some of the inputs are `~astropy.units.Quantity`
objects or if the ``data`` and ``bkg_error`` units differ.
The total error array, :math:`\\sigma_{\\mathrm{tot}}` is:
.. math:: \\sigma_{\\mathrm{tot}} = \\sqrt{\\sigma_{\\mathrm{b}}^2 +
\\frac{I}{g}}
where :math:`\\sigma_b`, :math:`I`, and :math:`g` are the background
``bkg_error`` image, ``data`` image, and ``effective_gain``,
respectively.
Pixels where ``data`` (:math:`I_i)` is negative do not contribute
additional Poisson noise to the total error, i.e.
:math:`\\sigma_{\\mathrm{tot}, i} = \\sigma_{\\mathrm{b}, i}`. Note
that this is different from `SExtractor`_, which sums the total
variance in the segment, including pixels where :math:`I_i` is
negative. In such cases, `SExtractor`_ underestimates the total
errors. Also note that SExtractor computes Poisson errors from
background-subtracted data, which also results in an underestimation
of the Poisson noise.
``effective_gain`` can either be a scalar value or a 2D image with
the same shape as the ``data``. A 2D image is useful with mosaic
images that have variable depths (i.e., exposure times) across the
field. For example, one should use an exposure-time map as the
``effective_gain`` for a variable depth mosaic image in count-rate
units.
As an example, if your input ``data`` are in units of ADU, then
``effective_gain`` should be in units of electrons/ADU (or
photons/ADU). If your input ``data`` are in units of electrons/s
then ``effective_gain`` should be the exposure time or an exposure
time map (e.g., for mosaics with non-uniform exposure times).
.. _SExtractor: http://www.astromatic.net/software/sextractor
"""
data = np.asanyarray(data)
bkg_error = np.asanyarray(bkg_error)
inputs = [data, bkg_error, effective_gain]
has_unit = [hasattr(x, 'unit') for x in inputs]
use_units = all(has_unit)
if any(has_unit) and not use_units:
raise ValueError('If any of data, bkg_error, or effective_gain has '
'units, then they all must all have units.')
if use_units:
if data.unit != bkg_error.unit:
raise ValueError('data and bkg_error must have the same units.')
count_units = [u.electron, u.photon]
datagain_unit = data.unit * effective_gain.unit
if datagain_unit not in count_units:
raise u.UnitsError('(data * effective_gain) has units of "{0}", '
'but it must have count units (e.g. '
'u.electron or u.photon).'
.format(datagain_unit))
if not isiterable(effective_gain):
effective_gain = np.zeros(data.shape) + effective_gain
else:
effective_gain = np.asanyarray(effective_gain)
if effective_gain.shape != data.shape:
raise ValueError('If input effective_gain is 2D, then it must '
'have the same shape as the input data.')
if np.any(effective_gain <= 0):
raise ValueError('effective_gain must be strictly positive '
'everywhere.')
# This calculation assumes that data and bkg_error have the same
# units. source_variance is calculated to have units of
# (data.unit)**2 so that it can be added with bkg_error**2 below. The
# final returned error will have units of data.unit. np.maximum is
# used to ensure that negative data values do not contribute to the
# Poisson noise.
if use_units:
unit = data.unit
data = data.value
effective_gain = effective_gain.value
source_variance = np.maximum(data / effective_gain, 0) * unit**2
else:
source_variance = np.maximum(data / effective_gain, 0)
return np.sqrt(bkg_error**2 + source_variance) | [
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array with the Poisson noise of sources.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The data array.
bkg_error : array_like or `~astropy.units.Quantity`
The pixel-wise Gaussian 1-sigma background-only errors of the
input ``data``. ``bkg_error`` should include all sources of
"background" error but *exclude* the Poisson error of the
sources. ``bkg_error`` must have the same shape as ``data``.
If ``data`` and ``bkg_error`` are `~astropy.units.Quantity`
objects, then they must have the same units.
effective_gain : float, array-like, or `~astropy.units.Quantity`
Ratio of counts (e.g., electrons or photons) to the units of
``data`` used to calculate the Poisson error of the sources.
Returns
-------
total_error : `~numpy.ndarray` or `~astropy.units.Quantity`
The total error array. If ``data``, ``bkg_error``, and
``effective_gain`` are all `~astropy.units.Quantity` objects,
then ``total_error`` will also be returned as a
`~astropy.units.Quantity` object with the same units as the
input ``data``. Otherwise, a `~numpy.ndarray` will be returned.
Notes
-----
To use units, ``data``, ``bkg_error``, and ``effective_gain`` must
*all* be `~astropy.units.Quantity` objects. ``data`` and
``bkg_error`` must have the same units. A `ValueError` will be
raised if only some of the inputs are `~astropy.units.Quantity`
objects or if the ``data`` and ``bkg_error`` units differ.
The total error array, :math:`\\sigma_{\\mathrm{tot}}` is:
.. math:: \\sigma_{\\mathrm{tot}} = \\sqrt{\\sigma_{\\mathrm{b}}^2 +
\\frac{I}{g}}
where :math:`\\sigma_b`, :math:`I`, and :math:`g` are the background
``bkg_error`` image, ``data`` image, and ``effective_gain``,
respectively.
Pixels where ``data`` (:math:`I_i)` is negative do not contribute
additional Poisson noise to the total error, i.e.
:math:`\\sigma_{\\mathrm{tot}, i} = \\sigma_{\\mathrm{b}, i}`. Note
that this is different from `SExtractor`_, which sums the total
variance in the segment, including pixels where :math:`I_i` is
negative. In such cases, `SExtractor`_ underestimates the total
errors. Also note that SExtractor computes Poisson errors from
background-subtracted data, which also results in an underestimation
of the Poisson noise.
``effective_gain`` can either be a scalar value or a 2D image with
the same shape as the ``data``. A 2D image is useful with mosaic
images that have variable depths (i.e., exposure times) across the
field. For example, one should use an exposure-time map as the
``effective_gain`` for a variable depth mosaic image in count-rate
units.
As an example, if your input ``data`` are in units of ADU, then
``effective_gain`` should be in units of electrons/ADU (or
photons/ADU). If your input ``data`` are in units of electrons/s
then ``effective_gain`` should be the exposure time or an exposure
time map (e.g., for mosaics with non-uniform exposure times).
.. _SExtractor: http://www.astromatic.net/software/sextractor | [
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astropy/photutils | photutils/aperture/rectangle.py | RectangularAperture.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyRectangularAperture` object
defined in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyRectangularAperture` object
A `SkyRectangularAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyRectangularAperture(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyRectangularAperture` object
defined in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyRectangularAperture` object
A `SkyRectangularAperture` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyRectangularAperture(**sky_params) | [
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The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
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astropy/photutils | photutils/aperture/rectangle.py | RectangularAnnulus.to_sky | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyRectangularAnnulus` object
defined in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
-------
aperture : `SkyRectangularAnnulus` object
A `SkyRectangularAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyRectangularAnnulus(**sky_params) | python | def to_sky(self, wcs, mode='all'):
"""
Convert the aperture to a `SkyRectangularAnnulus` object
defined in celestial coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
(``'all'``; default) or only including only the core WCS
transformation (``'wcs'``).
Returns
-------
aperture : `SkyRectangularAnnulus` object
A `SkyRectangularAnnulus` object.
"""
sky_params = self._to_sky_params(wcs, mode=mode)
return SkyRectangularAnnulus(**sky_params) | [
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astropy/photutils | photutils/aperture/rectangle.py | SkyRectangularAperture.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `RectangularAperture` object defined
in pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
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aperture : `RectangularAperture` object
A `RectangularAperture` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return RectangularAperture(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `RectangularAperture` object defined
in pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
-------
aperture : `RectangularAperture` object
A `RectangularAperture` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
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astropy/photutils | photutils/aperture/rectangle.py | SkyRectangularAnnulus.to_pixel | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `RectangularAnnulus` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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-------
aperture : `RectangularAnnulus` object
A `RectangularAnnulus` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return RectangularAnnulus(**pixel_params) | python | def to_pixel(self, wcs, mode='all'):
"""
Convert the aperture to a `RectangularAnnulus` object defined in
pixel coordinates.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The world coordinate system (WCS) transformation to use.
mode : {'all', 'wcs'}, optional
Whether to do the transformation including distortions
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Returns
-------
aperture : `RectangularAnnulus` object
A `RectangularAnnulus` object.
"""
pixel_params = self._to_pixel_params(wcs, mode=mode)
return RectangularAnnulus(**pixel_params) | [
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Whether to do the transformation including distortions
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