discretize_model

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

Function to evaluate analytical models on a grid.

Parameters:

model : FittableModel

Model to be evaluated.

x_range : tuple

x range in which the model is evaluated.

y_range : tuple, optional

y range in which the model is evaluated. 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:

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()

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