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The arrays are added with the centers lying upon each other. ii(tsizetcopytslice(tarray_1tarray_2t new_arraytcentertslice_((s8lib/python2.7/site-packages/astropy/convolution/utils.pytadd_kernel_arrays_1Ds    cC`sœ|j|jkrÊ|jƒ}g|jD]}|d^q(}t|d|jdd|d|jdddƒ}t|d|jdd|d|jdddƒ}|||fc|7<|S|j|jkr”|jƒ}g|jD]}|d^qò}t|d|jdd|d|jdddƒ}t|d|jdd|d|jdddƒ}|||fc|7<|S||S(uw Add two 2D kernel arrays of different size. The arrays are added with the centers lying upon each other. iii(R R tshapeR(RRRt axes_sizeRtslice_xtslice_y((s8lib/python2.7/site-packages/astropy/convolution/utils.pytadd_kernel_arrays_2D0s&        ucenteri cC`s1t|ƒstdƒ‚nt|tƒs<t|ƒƒ}n|j}|dkr`tdƒ‚nttj |ƒƒj ƒsŠtdƒ‚n|r½ttj |ƒƒj ƒs½tdƒ‚q½n|dkrä|dkrätdƒ‚n|dkr |dk r tdƒ‚n|d krO|dkr0t ||ƒS|dkr-t |||ƒSnÞ|d kr“|dkrtt||ƒS|dkr-t|||ƒSnš|d krÝ|dkr»t|||ƒS|dkr-t||||ƒSnP|d kr!|dkrt||ƒS|dkr-t|||ƒSn td ƒ‚dS(uÊ Function to evaluate analytical model functions on a grid. So far the function can only deal with pixel coordinates. Parameters ---------- model : `~astropy.modeling.FittableModel` or callable. Analytic model function to be discretized. Callables, which are not an instances of `~astropy.modeling.FittableModel` are passed to `~astropy.modeling.custom_model` and then evaluated. x_range : tuple x range in which the model is evaluated. The difference between the upper an lower limit must be a whole number, so that the output array size is well defined. y_range : tuple, optional y range in which the model is evaluated. The difference between the upper an lower limit must be a whole number, so that the output array size is well defined. Necessary only for 2D models. mode : str, optional One of the following 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. For 2D models interpolation is bilinear. * ``'oversample'`` Discretize model by taking the average on an oversampled grid. * ``'integrate'`` Discretize model by integrating the model over the bin using `scipy.integrate.quad`. Very slow. factor : float or int Factor of oversampling. Default = 10. Returns ------- array : `numpy.array` Model value array Notes ----- The ``oversample`` mode allows to conserve the integral on a subpixel scale. Here is the example of a normalized Gaussian1D: .. plot:: :include-source: import matplotlib.pyplot as plt import numpy as np from astropy.modeling.models import Gaussian1D from astropy.convolution.utils import discretize_model gauss_1D = Gaussian1D(1 / (0.5 * np.sqrt(2 * np.pi)), 0, 0.5) y_center = discretize_model(gauss_1D, (-2, 3), mode='center') y_corner = discretize_model(gauss_1D, (-2, 3), mode='linear_interp') y_oversample = discretize_model(gauss_1D, (-2, 3), mode='oversample') plt.plot(y_center, label='center sum = {0:3f}'.format(y_center.sum())) plt.plot(y_corner, label='linear_interp sum = {0:3f}'.format(y_corner.sum())) plt.plot(y_oversample, label='oversample sum = {0:3f}'.format(y_oversample.sum())) plt.xlabel('pixels') plt.ylabel('value') plt.legend() plt.show() uModel must be callable.iu2discretize_model only supports 1-d and 2-d models.uTThe difference between the upper an lower limit of 'x_range' must be a whole number.uTThe difference between the upper an lower limit of 'y_range' must be a whole number.u'y range not specified, but model is 2-diu)y range specified, but model is only 1-d.ucenteru linear_interpu oversampleu integrateu Invalid mode.N(tcallablet TypeErrort isinstanceRRtn_inputst ValueErrortfloattnptdifft is_integertNonetdiscretize_center_1Dtdiscretize_center_2Dtdiscretize_linear_1Dtdiscretize_bilinear_2Dtdiscretize_oversample_1Dtdiscretize_oversample_2Dtdiscretize_integrate_1Dtdiscretize_integrate_2DR(tmodeltx_rangety_rangetmodetfactortndim((s8lib/python2.7/site-packages/astropy/convolution/utils.pytdiscretize_modelKsJF                  cC`stj|Œ}||ƒS(uH Discretize model by taking the value at the center of the bin. (R tarange(R,R-tx((s8lib/python2.7/site-packages/astropy/convolution/utils.pyR$¾scC`sCtj|Œ}tj|Œ}tj||ƒ\}}|||ƒS(uJ Discretize model by taking the value at the center of the pixel. 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