fit_fwhm#

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

Fit the FWHM of 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. 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.

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:
fwhmndarray

The FWHM of the sources. Note that the returned FWHM values are always positive.

See also

fit_2dgaussian

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

Notes

The source(s) are fit using the fit_2dgaussian() function, which uses a CircularGaussianPRF model with 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 the FWHM of a single source (e.g., a cutout image):

>>> import numpy as np
>>> from photutils.psf import CircularGaussianPRF, fit_fwhm
>>> 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)
>>> fwhm = fit_fwhm(data)
>>> fwhm  
array([3.123])

Fit the FWHMs of multiple sources in an image:

>>> import numpy as np
>>> from photutils.detection import DAOStarFinder
>>> from photutils.psf import (CircularGaussianPRF, fit_fwhm,
...                            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 = list(zip(sources['x_0'], sources['y_0']))
>>> fwhms = fit_fwhm(data, xypos=xypos, fit_shape=7)
>>> fwhms  
array([5.69467204, 5.21376414, 7.65508658, 3.20255356, 6.66003098])
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