fit_2dgaussian#

photutils.psf.fit_2dgaussian(data, *, xypos=None, fwhm=None, fix_fwhm=True, fit_shape=None, mask=None, error=None)[source]#

Fit a 2D Gaussian model to one or more sources in an image.

This convenience function uses a CircularGaussianPRF model to fit the sources using the PSFPhotometry class.

Non-finite values (e.g., NaN or inf) in the data array are automatically masked.

Parameters:

data2D array: The 2D array of the image. The input array must be background subtracted.
xyposarray-like, optional: The initial (x, y) pixel coordinates of the sources as a list of tuples or a 2D array. If None, then one source will be fit with an initial position using the center-of-mass centroid of the data array.
fwhmfloat, optional: The initial guess for the FWHM of the Gaussian PSF model. If None, then the initial guess is half the mean of the x and y sizes of the fit_shape values.
fix_fwhmbool, optional: Whether to fix the FWHM of the Gaussian PSF model during the fitting process.
fit_shapeint or tuple of two ints, optional: The shape of the fitting region. If a scalar, then it is assumed to be a square. If None, then the shape of the input data will be used.
maskarray-like (bool), optional: A boolean mask with the same shape as the input data, where a True value indicates the corresponding element of data is masked.
error2D array, optional: The pixel-wise Gaussian 1-sigma errors of the input data. error is assumed to include all sources of error, including the Poisson error of the sources (see calc_total_error). error must have the same shape as the input data. If a Quantity array, then data must also be a Quantity array with the same units.

Returns:

resultPSFPhotometry: The PSF-fitting photometry results.

See also

fit_fwhm: Fit the FWHM of one or more sources in an image.

Notes

The source(s) are fit with a CircularGaussianPRF model using the PSFPhotometry class. The initial guess for the flux is the sum of the pixel values within the fitting region. If fwhm is None, then the initial guess for the FWHM is half the mean of the x and y sizes of the fit_shape values.

Examples

Fit a 2D Gaussian model to an image containing only one source (e.g., a cutout image):

>>> import numpy as np
>>> from photutils.psf import CircularGaussianPRF, fit_2dgaussian
>>> yy, xx = np.mgrid[:51, :51]
>>> model = CircularGaussianPRF(x_0=22.17, y_0=28.87, fwhm=3.123, flux=9.7)
>>> data = model(xx, yy)
>>> fit = fit_2dgaussian(data, fix_fwhm=False, fit_shape=7)
>>> phot_tbl = fit.results
>>> cols = ['x_fit', 'y_fit', 'fwhm_fit', 'flux_fit']
>>> for col in cols:
...     phot_tbl[col].info.format = '.4f'  # optional format
>>> print(phot_tbl[['id'] + cols])
 id  x_fit   y_fit  fwhm_fit flux_fit
--- ------- ------- -------- --------
  1 22.1700 28.8700   3.1230   9.7000

Fit a 2D Gaussian model to multiple sources in an image:

>>> import numpy as np
>>> from photutils.detection import DAOStarFinder
>>> from photutils.psf import (CircularGaussianPRF, fit_2dgaussian,
...                            make_psf_model_image)
>>> model = CircularGaussianPRF()
>>> data, sources = make_psf_model_image((100, 100), model, 5,
...                                      min_separation=25,
...                                      model_shape=(15, 15),
...                                      flux=(100, 200), fwhm=[3, 8])
>>> finder = DAOStarFinder(0.1, 5)
>>> finder_tbl = finder(data)
>>> xypos = zip(sources['x_0'], sources['y_0'])
>>> psfphot = fit_2dgaussian(data, xypos=xypos, fit_shape=7,
...                          fix_fwhm=False)
>>> phot_tbl = psfphot.results
>>> len(phot_tbl)
5

Here we show only a few columns of the photometry table:

>>> cols = ['x_fit', 'y_fit', 'fwhm_fit', 'flux_fit']
>>> for col in cols:
...     phot_tbl[col].info.format = '.4f'  # optional format
>>> print(phot_tbl[['id'] + cols])
 id  x_fit   y_fit  fwhm_fit flux_fit
--- ------- ------- -------- --------
  1 61.7787 74.6905   5.6947 147.9988
  2 30.2017 27.5858   5.2138 123.2373
  3 10.5237 82.3776   7.6551 180.1881
  4  8.4214 12.0369   3.2026 192.3530
  5 76.9412 35.9061   6.6600 126.6130