/usr/lib/python2.7/dist-packages/photutils/datasets/make.py is in python-photutils 0.2.1-2.
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"""
Make example datasets.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from ..utils import check_random_state
from astropy.table import Table
from astropy.modeling.models import Gaussian2D
from astropy.convolution import discretize_model
import astropy.units as u
__all__ = ['make_noise_image', 'make_poisson_noise', 'make_gaussian_sources',
'make_random_gaussians', 'make_4gaussians_image',
'make_100gaussians_image']
def make_noise_image(image_shape, type='gaussian', mean=None, stddev=None,
unit=None, random_state=None):
"""
Make a noise image containing Gaussian or Poisson noise.
Parameters
----------
image_shape : 2-tuple of int
Shape of the output 2D image.
type : {'gaussian', 'poisson'}
The distribution used to generate the random noise.
* ``'gaussian'``: Gaussian distributed noise.
* ``'poisson'``: Poisson distributed nose.
mean : float
The mean of the random distribution. Required for both Gaussian
and Poisson noise.
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).
unit : `~astropy.units.UnitBase` instance, str
An object that represents the unit desired for the output image.
Must be an `~astropy.units.UnitBase` object or a string parseable
by the `~astropy.units` package.
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 : `~numpy.ndarray`
Image containing random noise.
See Also
--------
make_poisson_noise, make_gaussian_sources, make_random_gaussians
Examples
--------
.. plot::
:include-source:
# make a Gaussian and Poisson noise image
from photutils.datasets import make_noise_image
shape = (100, 200)
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(2, 1, figsize=(8, 8))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax2.imshow(image2, origin='lower', interpolation='nearest')
"""
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=image_shape)
elif type == 'poisson':
image = prng.poisson(lam=mean, size=image_shape)
else:
raise ValueError('Invalid type: {0}. Use one of '
'{"gaussian", "poisson"}.'.format(type))
if unit is not None:
image = u.Quantity(image, unit=unit)
return image
def make_poisson_noise(image, random_state=None):
"""
Make a Poisson noise image from an image whose pixel values
represent the expected number of counts (e.g., electrons or
photons). Each pixel in the output noise image is generated by
drawing a random sample from a Poisson distribution with expectation
value given by the input ``image``.
Parameters
----------
image : `~numpy.ndarray` or `~astropy.units.Quantity`
The 2D image from which to make Poisson noise. Each pixel in the
image must have a positive value (e.g., electron or photon
counts).
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Returns
-------
image : `~numpy.ndarray` or `~astropy.units.Quantity`
The 2D image of Poisson noise. The image is generated by
drawing samples from a Poisson distribution with expectation
values for each pixel given by the input ``image``.
See Also
--------
make_noise_image, make_gaussian_sources, make_random_gaussians
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 and add a background level,
# then make the Poisson noise image.
from photutils.datasets import make_gaussian_sources
from photutils.datasets import make_poisson_noise
shape = (100, 200)
bkgrd = 10.
image1 = make_gaussian_sources(shape, table) + bkgrd
image2 = make_poisson_noise(image1, random_state=12345)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax2.imshow(image2, origin='lower', interpolation='nearest')
"""
prng = check_random_state(random_state)
if isinstance(image, u.Quantity):
return prng.poisson(image.value) * image.unit
else:
return prng.poisson(image)
def make_gaussian_sources(image_shape, source_table, oversample=1, unit=None,
hdu=False, wcs=False, wcsheader=None):
"""
Make an image containing 2D Gaussian sources.
Parameters
----------
image_shape : 2-tuple of int
Shape of the output 2D image.
source_table : `~astropy.table.Table`
Table of parameters for the Gaussian sources. Each row of the
table corresponds to a Gaussian source whose parameters are
defined by the column names. The column names must include
``flux`` or ``amplitude``, ``x_mean``, ``y_mean``, ``x_stddev``,
``y_stddev``, and ``theta`` (see
`~astropy.modeling.functional_models.Gaussian2D` for a
description of most of these parameter names). If both ``flux``
and ``amplitude`` are present, then ``amplitude`` will be
ignored.
oversample : float, optional
The sampling factor used to discretize the
`~astropy.modeling.functional_models.Gaussian2D` 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.
unit : `~astropy.units.UnitBase` instance, str, optional
An object that represents the unit desired for the output image.
Must be an `~astropy.units.UnitBase` object or a string
parseable by the `~astropy.units` package.
hdu : bool, optional
If `True` returns ``image`` as an `~astropy.io.fits.ImageHDU`
object. To include WCS information in the header, use the
``wcs`` and ``wcsheader`` inputs. Otherwise the header will be
minimal. Default is `False`.
wcs : bool, optional
If `True` and ``hdu=True``, then a simple WCS will be included
in the returned `~astropy.io.fits.ImageHDU` header. Default is
`False`.
wcsheader : dict or `None`, optional
If ``hdu`` and ``wcs`` are `True`, this dictionary is passed to
`~astropy.wcs.WCS` to generate the returned
`~astropy.io.fits.ImageHDU` header.
Returns
-------
image : `~numpy.ndarray` or `~astropy.units.Quantity` or `~astropy.io.fits.ImageHDU`
Image or `~astropy.io.fits.ImageHDU` containing 2D Gaussian
sources.
See Also
--------
make_random_gaussians, make_noise_image, make_poisson_noise
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 without noise, with Gaussian
# noise, and with Poisson noise
from photutils.datasets import make_gaussian_sources
from photutils.datasets import make_noise_image
shape = (100, 200)
image1 = make_gaussian_sources(shape, table)
image2 = image1 + make_noise_image(shape, type='gaussian', mean=5.,
stddev=5.)
image3 = image1 + make_noise_image(shape, type='poisson', mean=5.)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 12))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax3.imshow(image3, origin='lower', interpolation='nearest')
"""
image = np.zeros(image_shape, dtype=np.float64)
y, x = np.indices(image_shape)
if 'flux' in source_table.colnames:
amplitude = source_table['flux'] / (2. * np.pi *
source_table['x_stddev'] *
source_table['y_stddev'])
elif 'amplitude' in source_table.colnames:
amplitude = source_table['amplitude']
else:
raise ValueError('either "amplitude" or "flux" must be columns in '
'the input source_table')
for i, source in enumerate(source_table):
model = Gaussian2D(amplitude=amplitude[i], x_mean=source['x_mean'],
y_mean=source['y_mean'],
x_stddev=source['x_stddev'],
y_stddev=source['y_stddev'], theta=source['theta'])
if oversample == 1:
image += model(x, y)
else:
image += discretize_model(model, (0, image_shape[1]),
(0, image_shape[0]), mode='oversample',
factor=oversample)
if unit is not None:
image = u.Quantity(image, unit=unit)
if wcs and not hdu:
raise ValueError("wcs header only works with hdu output, use keyword "
"'hdu=True'")
if hdu is True:
from astropy.io import fits
if wcs:
from astropy.wcs import WCS
if wcsheader is None:
# Go with the simplest valid header
header = WCS({'CTYPE1': 'RA---TAN',
'CTYPE2': 'DEC--TAN',
'CRPIX1': int(image_shape[1] / 2),
'CRPIX2': int(image_shape[0] / 2)}, ).to_header()
else:
header = WCS(wcsheader)
else:
header = None
image = fits.ImageHDU(image, header=header)
return image
def make_random_gaussians(n_sources, flux_range, xmean_range, ymean_range,
xstddev_range, ystddev_range, amplitude_range=None,
random_state=None):
"""
Make a `~astropy.table.Table` containing parameters for randomly
generated 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 bounds.
The output table can be input into `make_gaussian_sources` to create
an image containing the 2D Gaussian sources.
Parameters
----------
n_sources : float
The number of random Gaussian sources to generate.
flux_range : array-like
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source fluxes.
``flux_range`` will be ignored if ``amplitude_range`` is input.
xmean_range : array-like
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source ``x_mean``.
ymean_range : array-like
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source ``y_mean``.
xstddev_range : array-like
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source ``x_stddev``.
ystddev_range : array-like
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source ``y_stddev``.
amplitude_range : array-like, optional
The lower and upper boundaries, ``(lower, upper)``, of the
uniform distribution from which to draw source amplitudes. If
``amplitude_range`` is input, then ``flux_range`` will be
ignored.
random_state : int or `~numpy.random.RandomState`, optional
Pseudo-random number generator state used for random sampling.
Separate function calls with the same parameters and
``random_state`` will generate the identical sources.
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. The column
names will include ``flux`` or ``amplitude``, ``x_mean``,
``y_mean``, ``x_stddev``, ``y_stddev``, and ``theta`` (see
`~astropy.modeling.functional_models.Gaussian2D` for a
description of most of these parameter names).
See Also
--------
make_gaussian_sources, make_noise_image, make_poisson_noise
Examples
--------
.. plot::
:include-source:
# create the random sources
from photutils.datasets import make_random_gaussians
n_sources = 100
flux_range = [500, 1000]
xmean_range = [0, 500]
ymean_range = [0, 300]
xstddev_range = [1, 5]
ystddev_range = [1, 5]
table = make_random_gaussians(n_sources, flux_range, xmean_range,
ymean_range, xstddev_range,
ystddev_range, random_state=12345)
# make an image of the random sources without noise, with
# Gaussian noise, and with Poisson noise
from photutils.datasets import make_gaussian_sources
from photutils.datasets import make_noise_image
shape = (300, 500)
image1 = make_gaussian_sources(shape, table)
image2 = image1 + make_noise_image(shape, type='gaussian', mean=5.,
stddev=2.)
image3 = image1 + make_noise_image(shape, type='poisson', mean=5.)
# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 12))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax3.imshow(image3, origin='lower', interpolation='nearest')
"""
prng = check_random_state(random_state)
sources = Table()
if amplitude_range is None:
sources['flux'] = prng.uniform(flux_range[0], flux_range[1], n_sources)
else:
sources['amplitude'] = prng.uniform(amplitude_range[0],
amplitude_range[1], n_sources)
sources['x_mean'] = prng.uniform(xmean_range[0], xmean_range[1], n_sources)
sources['y_mean'] = prng.uniform(ymean_range[0], ymean_range[1], n_sources)
sources['x_stddev'] = prng.uniform(xstddev_range[0], xstddev_range[1],
n_sources)
sources['y_stddev'] = prng.uniform(ystddev_range[0], ystddev_range[1],
n_sources)
sources['theta'] = prng.uniform(0, 2.*np.pi, n_sources)
return sources
def make_4gaussians_image(hdu=False, wcs=False, wcsheader=None):
"""
Make an example image containing four 2D Gaussians plus Gaussian
noise.
The background has a mean and standard deviation of 5.
Parameters
----------
hdu : bool, optional
If `True` returns ``image`` as an `~astropy.io.fits.ImageHDU`
object. To include WCS information in the header, use the
``wcs`` and ``wcsheader`` inputs. Otherwise the header will be
minimal. Default is `False`.
wcs : bool, optional
If `True` and ``hdu=True``, then a simple WCS will be included
in the returned `~astropy.io.fits.ImageHDU` header. Default is
`False`.
wcsheader : dict or `None`, optional
If ``hdu`` and ``wcs`` are `True`, this dictionary is passed to
`~astropy.wcs.WCS` to generate the returned
`~astropy.io.fits.ImageHDU` header.
Returns
-------
image : `~numpy.ndarray` or `~astropy.io.fits.ImageHDU`
Image or `~astropy.io.fits.ImageHDU` containing 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', cmap='gray')
"""
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)
sources = make_gaussian_sources(shape, table, hdu=hdu,
wcs=wcs, wcsheader=wcsheader)
noise = make_noise_image(shape, type='gaussian', mean=5.,
stddev=5., random_state=12345)
if hdu is True:
sources.data += noise
data = sources
else:
data = (sources + noise)
return data
def make_100gaussians_image():
"""
Make an example image containing 100 2D Gaussians plus Gaussian
noise.
The background has a mean of 5 and a standard deviation of 2.
Returns
-------
image : `~numpy.ndarray`
Image containing 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', cmap='gray')
"""
n_sources = 100
flux_range = [500, 1000]
xmean_range = [0, 500]
ymean_range = [0, 300]
xstddev_range = [1, 5]
ystddev_range = [1, 5]
table = make_random_gaussians(n_sources, flux_range, xmean_range,
ymean_range, xstddev_range,
ystddev_range, random_state=12345)
shape = (300, 500)
image1 = make_gaussian_sources(shape, table)
image2 = image1 + make_noise_image(shape, type='gaussian', mean=5.,
stddev=2., random_state=12345)
return image2
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