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- Astropy v5.2.1 »
- Convolution and Filtering (`astropy.convolution`) »
- Convolving with Unnormalized Kernels

# Convolving with Unnormalized Kernels

There are some tasks, such as source finding, where you want to apply a filter with a kernel that is not normalized.

For data that are well-behaved (contain no missing or infinite values), this can be done in one step:

    convolve(image, kernel)

## Examples

For an example of applying a filter with a kernel that is not normalized, we can try to run a commonly used peak enhancing kernel:

    import numpy as np
    import matplotlib.pyplot as plt

    from astropy.io import fits
    from astropy.utils.data import get_pkg_data_filename
    from astropy.convolution import CustomKernel
    from scipy.signal import convolve as scipy_convolve
    from astropy.convolution import convolve, convolve_fft

    # Load the data from data.astropy.org
    filename = get_pkg_data_filename('galactic_center/gc_msx_e.fits')
    hdu = fits.open(filename)[0]

    # Scale the file to have reasonable numbers
    # (this is mostly so that colorbars don't have too many digits)
    # Also, we crop it so you can see individual pixels
    img = hdu.data[50:90, 60:100] * 1e5

    kernel = CustomKernel([[-1,-1,-1], [-1, 8, -1], [-1,-1,-1]])

    astropy_conv = convolve(img, kernel, normalize_kernel=False, nan_treatment='fill')
    #astropy_conv_fft = convolve_fft(img, kernel, normalize_kernel=False, nan_treatment='fill')

    plt.figure(1, figsize=(12, 12)).clf()
    ax1 = plt.subplot(1, 2, 1)
    im = ax1.imshow(img, vmin=-6., vmax=5.e1, origin='lower',
                    interpolation='nearest', cmap='viridis')

    ax2 = plt.subplot(1, 2, 2)
    im = ax2.imshow(astropy_conv, vmin=-6., vmax=5.e1, origin='lower',
                    interpolation='nearest', cmap='viridis')

(png, svg, pdf)

If you have an image with missing values (NaNs), you have to replace them with real values first. Often, the best way to do this is to replace the NaN values with interpolated values. In the example below, we use a Gaussian kernel with a size similar to that of our peak-finding kernel to replace the bad data before applying the peak-finding kernel.

    from astropy.convolution import Gaussian2DKernel, interpolate_replace_nans

    # Select a random set of pixels that were affected by some sort of artifact
    # and replaced with NaNs (e.g., cosmic-ray-affected pixels)
    rng = np.random.default_rng(42)
    yinds, xinds = np.indices(img.shape)
    img[rng.choice(yinds.flat, 50), rng.choice(xinds.flat, 50)] = np.nan

    # We smooth with a Gaussian kernel with x_stddev=1 (and y_stddev=1)
    # It is a 9x9 array
    kernel = Gaussian2DKernel(x_stddev=1)

    # interpolate away the NaNs
    reconstructed_image = interpolate_replace_nans(img, kernel)

    # apply peak-finding
    kernel = CustomKernel([[-1,-1,-1], [-1, 8, -1], [-1,-1,-1]])

    # Use the peak-finding kernel
    # We have to turn off kernel normalization and set nan_treatment to "fill"
    # here because `nan_treatment='interpolate'` is incompatible with non-
    # normalized kernels
    peaked_image = convolve(reconstructed_image, kernel,
                            normalize_kernel=False,
                            nan_treatment='fill')

    plt.figure(1, figsize=(12, 12)).clf()
    ax1 = plt.subplot(1, 3, 1)
    ax1.set_title("Image with missing data")
    im = ax1.imshow(img, vmin=-6., vmax=5.e1, origin='lower',
                    interpolation='nearest', cmap='viridis')

    ax2 = plt.subplot(1, 3, 2)
    ax2.set_title("Interpolated")
    im = ax2.imshow(reconstructed_image, vmin=-6., vmax=5.e1, origin='lower',
                    interpolation='nearest', cmap='viridis')

    ax3 = plt.subplot(1, 3, 3)
    ax3.set_title("Peak-Finding")
    im = ax3.imshow(peaked_image, vmin=-6., vmax=5.e1, origin='lower',
                    interpolation='nearest', cmap='viridis')

(png, svg, pdf)