PSFKing¶
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class
gammapy.irf.PSFKing(energy_lo, energy_hi, offset, gamma, sigma, energy_thresh_lo=<Quantity 0.1 TeV>, energy_thresh_hi=<Quantity 100. TeV>)[source]¶ Bases:
objectKing profile analytical PSF depending on energy and offset.
This PSF parametrisation and FITS data format is described here: PSF_KING.
Parameters: Methods Summary
evaluate(self[, energy, offset])Evaluate analytic PSF parameters at a given energy and offset. evaluate_direct(r, gamma, sigma)Evaluate the PSF model. from_table(table)Create PSFKingfromTable.info(self)Print some basic info. read(filename[, hdu])Create PSFKingfrom FITS file.to_energy_dependent_table_psf(self[, theta, …])Convert to energy-dependent table PSF. to_fits(self)Convert PSF table data to FITS HDU list. write(self, filename, \*args, \*\*kwargs)Write PSF to FITS file. Methods Documentation
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evaluate(self, energy=None, offset=None)[source]¶ Evaluate analytic PSF parameters at a given energy and offset.
Uses nearest-neighbor interpolation.
Parameters: Returns: - values :
Quantity Interpolated value
- values :
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static
evaluate_direct(r, gamma, sigma)[source]¶ Evaluate the PSF model.
Formula is given here: PSF_KING.
Parameters: Returns: - psf_value :
Quantity PSF value
- psf_value :
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classmethod
read(filename, hdu=1)[source]¶ Create
PSFKingfrom FITS file.Parameters: - filename : str
File name
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to_energy_dependent_table_psf(self, theta=None, rad=None, exposure=None)[source]¶ Convert to energy-dependent table PSF.
Parameters: Returns: - table_psf :
EnergyDependentTablePSF Energy-dependent PSF
- table_psf :
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