B q\@sddlZddlZddlmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZdd d d d d dddddddddgZddZGdddeZGdd d eZGdd d eZGdd d eZGdd d eZGdddeZGdddeZGd ddeZGd!ddeZGd"ddeZGd#ddeZGd$ddeZGd%ddeZGd&ddeZ Gd'd(d(eZ!Gd)d d eZ"dS)*N)Kernel1DKernel2DKernel) has_even_axisraise_even_kernel_exception)models)Fittable1DModelFittable2DModel)deprecated_renamed_argumentGaussian1DKernelGaussian2DKernel CustomKernel Box1DKernel Box2DKernelTophat2DKernelTrapezoid1DKernelMexicanHat1DKernelMexicanHat2DKernelAiryDisk2DKernelMoffat2DKernel Model1DKernel Model2DKernelTrapezoidDisk2DKernel Ring2DKernelcCs&t|}|ddkr|dS|SdS)Nrr)mathZceil)valueir:lib/python3.7/site-packages/astropy/convolution/kernels.py_round_up_to_odd_integers  r!cs(eZdZdZdZdZfddZZS)r a  1D Gaussian filter kernel. The Gaussian filter is a filter with great smoothing properties. It is isotropic and does not produce artifacts. Parameters ---------- stddev : number Standard deviation of the Gaussian kernel. x_size : odd int, optional Size of the kernel array. Default = 8 * stddev mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by linearly interpolating between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. Very slow. factor : number, optional Factor of oversampling. Default factor = 10. If the factor is too large, evaluation can be very slow. See Also -------- Box1DKernel, Trapezoid1DKernel, MexicanHat1DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Gaussian1DKernel gauss_1D_kernel = Gaussian1DKernel(10) plt.plot(gauss_1D_kernel, drawstyle='steps') plt.xlabel('x [pixels]') plt.ylabel('value') plt.show() TFc sZtdtdtj|d||_td||_tj f|t d|j |_ dS)Ng?rr)rZ Gaussian1Dnpsqrtpi_modelr! _default_sizesuper__init__abs_arraysum _truncation)selfstddevkwargs) __class__rr r)Ss  zGaussian1DKernel.__init__)__name__ __module__ __qualname____doc__ _separable_is_boolr) __classcell__rr)r1r r s3cs6eZdZdZdZdZedddd fd d ZZS) r a 2D Gaussian filter kernel. The Gaussian filter is a filter with great smoothing properties. It is isotropic and does not produce artifacts. Parameters ---------- x_stddev : float Standard deviation of the Gaussian in x before rotating by theta. y_stddev : float Standard deviation of the Gaussian in y before rotating by theta. theta : float Rotation angle in radians. The rotation angle increases counterclockwise. x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * stddev. y_size : odd int, optional Size in y direction of the kernel array. Default = 8 * stddev. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Box2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Gaussian2DKernel gaussian_2D_kernel = Gaussian2DKernel(10) plt.imshow(gaussian_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() TFr/x_stddevz3.0Nc sv|dkr |}tjddtj||dd|||d|_tdt||g|_tj f|t d|j |_ dS)Ng?rr)r9y_stddevthetar")rZ Gaussian2Dr#r%r&r!maxr'r(r)r*r+r,r-)r.r9r;r<r0)r1rr r)s zGaussian2DKernel.__init__)Nr:) r2r3r4r5r6r7r r)r8rr)r1r r [s ; cs(eZdZdZdZdZfddZZS)ra 1D Box filter kernel. The Box filter or running mean is a smoothing filter. It is not isotropic and can produce artifacts, when applied repeatedly to the same data. By default the Box kernel uses the ``linear_interp`` discretization mode, which allows non-shifting, even-sized kernels. This is achieved by weighting the edge pixels with 1/2. E.g a Box kernel with an effective smoothing of 4 pixel would have the following array: [0.5, 1, 1, 1, 0.5]. Parameters ---------- width : number Width of the filter kernel. mode : str, optional One of the following discretization modes: * 'center' Discretize model by taking the value at the center of the bin. * 'linear_interp' (default) Discretize model by linearly interpolating between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian1DKernel, Trapezoid1DKernel, MexicanHat1DKernel Examples -------- Kernel response function: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Box1DKernel box_1D_kernel = Box1DKernel(9) plt.plot(box_1D_kernel, drawstyle='steps') plt.xlim(-1, 9) plt.xlabel('x [pixels]') plt.ylabel('value') plt.show() Tc sFtd|d||_t||_d|d<tjf|d|_|dS)Ng?r linear_interpmode) rZBox1Dr&r!r'r(r)r- normalize)r.widthr0)r1rr r)s  zBox1DKernel.__init__)r2r3r4r5r6r7r)r8rr)r1r rs7cs(eZdZdZdZdZfddZZS)ra 2D Box filter kernel. The Box filter or running mean is a smoothing filter. It is not isotropic and can produce artifact, when applied repeatedly to the same data. By default the Box kernel uses the ``linear_interp`` discretization mode, which allows non-shifting, even-sized kernels. This is achieved by weighting the edge pixels with 1/2. Parameters ---------- width : number Width of the filter kernel. mode : str, optional One of the following discretization modes: * 'center' Discretize model by taking the value at the center of the bin. * 'linear_interp' (default) Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Box2DKernel box_2D_kernel = Box2DKernel(9) plt.imshow(box_2D_kernel, interpolation='none', origin='lower', vmin=0.0, vmax=0.015) plt.xlim(-1, 9) plt.ylim(-1, 9) plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() Tc sNtd|ddd|||_t||_d|d<tjf|d|_|dS)Ng?rrr>r?) rZBox2Dr&r!r'r(r)r-r@)r.rAr0)r1rr r)(s  zBox2DKernel.__init__)r2r3r4r5r6r7r)r8rr)r1r rs9cs eZdZdZfddZZS)ra 2D Tophat filter kernel. The Tophat filter is an isotropic smoothing filter. It can produce artifacts when applied repeatedly on the same data. Parameters ---------- radius : int Radius of the filter kernel. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Tophat2DKernel tophat_2D_kernel = Tophat2DKernel(40) plt.imshow(tophat_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() c sFtdtj|ddd||_td||_tjf|d|_ dS)Ng?rr) rZDisk2Dr#r%r&r!r'r(r)r-)r.radiusr0)r1rr r)ds zTophat2DKernel.__init__)r2r3r4r5r)r8rr)r1r r1s2cs eZdZdZfddZZS)ra) 2D Ring filter kernel. The Ring filter kernel is the difference between two Tophat kernels of different width. This kernel is useful for, e.g., background estimation. Parameters ---------- radius_in : number Inner radius of the ring kernel. width : number Width of the ring kernel. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Ring2DKernel ring_2D_kernel = Ring2DKernel(9, 8) plt.imshow(ring_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() c sX||}tdtj|d|ddd|||_td||_tjf|d|_ dS)Ng?rr) rZRing2Dr#r%r&r!r'r(r)r-)r.Z radius_inrAr0Z radius_out)r1rr r)s zRing2DKernel.__init__)r2r3r4r5r)r8rr)r1r rks2cs&eZdZdZdZdfdd ZZS)ra 1D trapezoid kernel. Parameters ---------- width : number Width of the filter kernel, defined as the width of the constant part, before it begins to slope down. slope : number Slope of the filter kernel's tails mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by linearly interpolating between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Box1DKernel, Gaussian1DKernel, MexicanHat1DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Trapezoid1DKernel trapezoid_1D_kernel = Trapezoid1DKernel(17, slope=0.2) plt.plot(trapezoid_1D_kernel, drawstyle='steps') plt.xlabel('x [pixels]') plt.ylabel('amplitude') plt.xlim(-1, 28) plt.show() F?c sDtdd|||_t|d||_tjf|d|_|dS)Nrrg@) rZ Trapezoid1Dr&r!r'r(r)r-r@)r.rAsloper0)r1rr r)s zTrapezoid1DKernel.__init__)rC)r2r3r4r5r7r)r8rr)r1r rs/cs&eZdZdZdZdfdd ZZS)ra 2D trapezoid kernel. Parameters ---------- radius : number Width of the filter kernel, defined as the width of the constant part, before it begins to slope down. slope : number Slope of the filter kernel's tails mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import TrapezoidDisk2DKernel trapezoid_2D_kernel = TrapezoidDisk2DKernel(20, slope=0.2) plt.imshow(trapezoid_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() F?c sJtddd|||_td|d||_tjf|d|_|dS)Nrrrg@) rZTrapezoidDisk2Dr&r!r'r(r)r-r@)r.rBrDr0)r1rr r)s zTrapezoidDisk2DKernel.__init__)rE)r2r3r4r5r7r)r8rr)r1r rs1cs$eZdZdZdZfddZZS)ra 1D Mexican hat filter kernel. The Mexican Hat, or inverted Gaussian-Laplace filter, is a bandpass filter. It smooths the data and removes slowly varying or constant structures (e.g. Background). It is useful for peak or multi-scale detection. This kernel is derived from a normalized Gaussian function, by computing the second derivative. This results in an amplitude at the kernels center of 1. / (sqrt(2 * pi) * width ** 3). The normalization is the same as for `scipy.ndimage.gaussian_laplace`, except for a minus sign. Parameters ---------- width : number Width of the filter kernel, defined as the standard deviation of the Gaussian function from which it is derived. x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * width. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by linearly interpolating between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Box1DKernel, Gaussian1DKernel, Trapezoid1DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import MexicanHat1DKernel mexicanhat_1D_kernel = MexicanHat1DKernel(10) plt.plot(mexicanhat_1D_kernel, drawstyle='steps') plt.xlabel('x [pixels]') plt.ylabel('value') plt.show() Tc sfdtdtj|d}t|d||_td||_tj f|t |j |j j |_dS)Ng?rrr")r#r$r%rZ MexicanHat1Dr&r!r'r(r)r*r+r,sizer-)r.rAr0 amplitude)r1rr r)[s zMexicanHat1DKernel.__init__)r2r3r4r5r7r)r8rr)r1r rs;cs$eZdZdZdZfddZZS)ra 2D Mexican hat filter kernel. The Mexican Hat, or inverted Gaussian-Laplace filter, is a bandpass filter. It smooths the data and removes slowly varying or constant structures (e.g. Background). It is useful for peak or multi-scale detection. This kernel is derived from a normalized Gaussian function, by computing the second derivative. This results in an amplitude at the kernels center of 1. / (pi * width ** 4). The normalization is the same as for `scipy.ndimage.gaussian_laplace`, except for a minus sign. Parameters ---------- width : number Width of the filter kernel, defined as the standard deviation of the Gaussian function from which it is derived. x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * width. y_size : odd int, optional Size in y direction of the kernel array. Default = 8 * width. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, Tophat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import MexicanHat2DKernel mexicanhat_2D_kernel = MexicanHat2DKernel(10) plt.imshow(mexicanhat_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() Fc s^dtj|d}t|dd||_td||_tjf|t |j |j j |_ dS)Ng?rr")r#r%rZ MexicanHat2Dr&r!r'r(r)r*r+r,rGr-)r.rAr0rH)r1rr r)s zMexicanHat2DKernel.__init__)r2r3r4r5r7r)r8rr)r1r rcs>cs$eZdZdZdZfddZZS)ra 2D Airy disk kernel. This kernel models the diffraction pattern of a circular aperture. This kernel is normalized to a peak value of 1. Parameters ---------- radius : float The radius of the Airy disk kernel (radius of the first zero). x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * radius. y_size : odd int, optional Size in y direction of the kernel array. Default = 8 * radius. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import AiryDisk2DKernel airydisk_2D_kernel = AiryDisk2DKernel(10) plt.imshow(airydisk_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() Fc s@tddd||_td||_tjf||d|_dS)Nrrr") rZ AiryDisk2Dr&r!r'r(r)r@r-)r.rBr0)r1rr r)s zAiryDisk2DKernel.__init__)r2r3r4r5r7r)r8rr)r1r rs4cs$eZdZdZdZfddZZS)ra 2D Moffat kernel. This kernel is a typical model for a seeing limited PSF. Parameters ---------- gamma : float Core width of the Moffat model. alpha : float Power index of the Moffat model. x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * radius. y_size : odd int, optional Size in y direction of the kernel array. Default = 8 * radius. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. See Also -------- Gaussian2DKernel, Box2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel Examples -------- Kernel response: .. plot:: :include-source: import matplotlib.pyplot as plt from astropy.convolution import Moffat2DKernel moffat_2D_kernel = Moffat2DKernel(3, 2) plt.imshow(moffat_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show() Fc s\|dtj||}t|dd|||_td|jj|_tj f|| d|_ dS)Ng?rg@) r#r%rZMoffat2Dr&r!Zfwhmr'r(r)r@r-)r.ZgammaZalphar0rH)r1rr r)#s zMoffat2DKernel.__init__)r2r3r4r5r7r)r8rr)r1r rs5cs(eZdZdZdZdZfddZZS)rak Create kernel from 1D model. The model has to be centered on x = 0. Parameters ---------- model : `~astropy.modeling.Fittable1DModel` Kernel response function model x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * width. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by linearly interpolating between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. Raises ------ TypeError If model is not an instance of `~astropy.modeling.Fittable1DModel` See also -------- Model2DKernel : Create kernel from `~astropy.modeling.Fittable2DModel` CustomKernel : Create kernel from list or array Examples -------- Define a Gaussian1D model: >>> from astropy.modeling.models import Gaussian1D >>> from astropy.convolution.kernels import Model1DKernel >>> gauss = Gaussian1D(1, 0, 2) And create a custom one dimensional kernel from it: >>> gauss_kernel = Model1DKernel(gauss, x_size=9) This kernel can now be used like a usual Astropy kernel. Fc s,t|tr||_ntdtjf|dS)NzMust be Fittable1DModel) isinstancer r& TypeErrorr(r))r.modelr0)r1rr r)fs zModel1DKernel.__init__)r2r3r4r5r6r7r)r8rr)r1r r.s4cs(eZdZdZdZdZfddZZS)ra Create kernel from 2D model. The model has to be centered on x = 0 and y = 0. Parameters ---------- model : `~astropy.modeling.Fittable2DModel` Kernel response function model x_size : odd int, optional Size in x direction of the kernel array. Default = 8 * width. y_size : odd int, optional Size in y direction of the kernel array. Default = 8 * width. mode : str, optional One of the following discretization modes: * 'center' (default) Discretize model by taking the value at the center of the bin. * 'linear_interp' Discretize model by performing a bilinear interpolation between the values at the corners of the bin. * 'oversample' Discretize model by taking the average on an oversampled grid. * 'integrate' Discretize model by integrating the model over the bin. factor : number, optional Factor of oversampling. Default factor = 10. Raises ------ TypeError If model is not an instance of `~astropy.modeling.Fittable2DModel` See also -------- Model1DKernel : Create kernel from `~astropy.modeling.Fittable1DModel` CustomKernel : Create kernel from list or array Examples -------- Define a Gaussian2D model: >>> from astropy.modeling.models import Gaussian2D >>> from astropy.convolution.kernels import Model2DKernel >>> gauss = Gaussian2D(1, 0, 0, 2, 2) And create a custom two dimensional kernel from it: >>> gauss_kernel = Model2DKernel(gauss, x_size=9) This kernel can now be used like a usual astropy kernel. Fc s2d|_t|tr||_ntdtjf|dS)NFzMust be Fittable2DModel)r6rJr r&rKr(r))r.rLr0)r1rr r)s  zModel2DKernel.__init__)r2r3r4r5r7r6r)r8rr)r1r rns7c@seZdZdZdZddZdS) PSFKernelz= Initialize filter kernel from astropy PSF instance. FcCs tddS)NzNot yet implemented)NotImplementedError)r.rrr r)szPSFKernel.__init__N)r2r3r4r5r6r)rrrr rMsrMcs:eZdZdZfddZeddZejddZZS)ra' Create filter kernel from list or array. Parameters ---------- array : list or array Filter kernel array. Size must be odd. Raises ------ TypeError If array is not a list or array. KernelSizeError If array size is even. See also -------- Model2DKernel, Model1DKernel Examples -------- Define one dimensional array: >>> from astropy.convolution.kernels import CustomKernel >>> import numpy as np >>> array = np.array([1, 2, 3, 2, 1]) >>> kernel = CustomKernel(array) >>> kernel.dimension 1 Define two dimensional array: >>> array = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]]) >>> kernel = CustomKernel(array) >>> kernel.dimension 2 cs||_t|jdS)N)arrayr(r)r+)r.rO)r1rr r)szCustomKernel.__init__cCs|jS)z& Filter kernel array. )r+)r.rrr rOszCustomKernel.arraycCst|tjr|tj|_n&t|tr:tj|tjd|_ntdt |rPt |jdk}|jdk}t t t |||_d|_dS)z, Filter kernel array setter )ZdtypezMust be list or array.g?rgN)rJr#ZndarrayZastypeZfloat64r+listrOrKrrboolallZ logical_orr7r-)r.rOZonesZzerosrrr rOs    ) r2r3r4r5r)propertyrOsetterr8rr)r1r rs%  )#rZnumpyr#ZcorerrrZutilsrrZastropy.modelingrZastropy.modeling.corer r Zastropy.utils.decoratorsr __all__r!r r rrrrrrrrrrrrrMrrrrr s8   ?LDF:<:<FI?C@D