discretize_model

astropy.convolution.discretize_model(model, x_range, y_range=None, mode='center', factor=10)

Evaluate an analytical model function on a pixel grid.

Parameters:

modelModel or callable.

Analytical model function to be discretized. A callable that is not a Model instance is converted to a model using custom_model.

x_range2-tuple

Lower and upper bounds of x pixel values at which the model is evaluated. The upper bound is non-inclusive. A x_range of (0, 3) means the model will be evaluated at x pixels 0, 1, and 2. The difference between the upper and lower bound must be a whole number so that the output array size is well defined.

y_range2-tuple or None, optional

Lower and upper bounds of y pixel values at which the model is evaluated. The upper bound is non-inclusive. A y_range of (0, 3) means the model will be evaluated at y pixels of 0, 1, and 2. The difference between the upper and lower bound must be a whole number so that the output array size is well defined. y_range is necessary only for 2D models.

mode{‘center’, ‘linear_interp’, ‘oversample’, ‘integrate’}, optional

One of the following modes:

• 'center' (default)

  Discretize model by taking the value at the center of the pixel bins.

• 'linear_interp'

  Discretize model by linearly interpolating between the values at the edges (1D) or corners (2D) of the pixel bins. For 2D models, the interpolation is bilinear.

• 'oversample'

  Discretize model by taking the average of model values in the pixel bins on an oversampled grid. Use the factor keyword to set the integer oversampling factor.

• 'integrate'

  Discretize model by integrating the model over the pixel bins using scipy.integrate.quad. This mode conserves the model integral on a subpixel scale, but is very slow.

factorint, optional

The integer oversampling factor used when mode='oversample'. Ignored otherwise.

Returns:

arraynumpy.ndarray

The discretized model array.

Examples

In this example, we define a Gaussian1D model that has been normalized so that it sums to 1.0. We then discretize this model using the 'center', 'linear_interp', and 'oversample' (with factor=10) modes.

(Source code, png, svg, pdf)