Source code for photutils.datasets.examples
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Tools for making simulated example images for documentation examples and
tests.
"""
import pathlib
import numpy as np
from astropy.modeling.models import Gaussian2D
from astropy.table import QTable
from astropy.utils.data import get_pkg_data_path
from photutils.datasets import make_model_image
from photutils.utils._deprecation import deprecated_positional_kwargs
__all__ = ['make_4gaussians_image', 'make_100gaussians_image']
_DATASETS_DATA_DIR = pathlib.Path(get_pkg_data_path('datasets', 'data',
package='photutils'))
def _make_gaussians_image(filename, shape, noise, noise_stddev,
**kwargs):
"""
Make an image containing 2D Gaussians from a parameter file.
Parameters
----------
filename : str
The name of the ECSV file containing the Gaussian parameters.
shape : 2-tuple of int
The shape of the output image.
noise : bool
Whether to include noise in the output image.
noise_stddev : float
The standard deviation of the Gaussian noise.
**kwargs
Additional keyword arguments passed to `make_model_image`.
Returns
-------
image : 2D `~numpy.ndarray`
Image containing 2D Gaussian sources.
"""
model = Gaussian2D()
params = QTable.read(_DATASETS_DATA_DIR / filename,
format='ascii.ecsv')
data = make_model_image(shape, model, params, x_name='x_mean',
y_name='y_mean', **kwargs)
data += 5.0 # background
if noise:
rng = np.random.default_rng(seed=0)
data += rng.normal(loc=0.0, scale=noise_stddev, size=shape)
return data
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
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:
import matplotlib.pyplot as plt
from astropy.visualization import simple_norm
from photutils.datasets import make_4gaussians_image
image = make_4gaussians_image()
fig, ax = plt.subplots()
norm = simple_norm(image, 'sqrt', percent=99.5)
ax.imshow(image, norm=norm, origin='lower')
"""
return _make_gaussians_image('4gaussians_params.ecsv', (100, 200),
noise, noise_stddev=5.0)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
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:
import matplotlib.pyplot as plt
from astropy.visualization import simple_norm
from photutils.datasets import make_100gaussians_image
image = make_100gaussians_image()
fig, ax = plt.subplots()
norm = simple_norm(image, 'sqrt', percent=99.5)
ax.imshow(image, norm=norm, origin='lower')
"""
return _make_gaussians_image('100gaussians_params.ecsv', (300, 500),
noise, noise_stddev=2.0, bbox_factor=6.0)
