Python convolution.Gaussian2DKernel方法代码示例

# 需要导入模块: from astropy import convolution [as 别名]
# 或者: from astropy.convolution import Gaussian2DKernel [as 别名]
def setup_method(self, method):
        np.random.seed(1000)  # so we always get the same images.

        self.min_, self.stretch_, self.Q = 0, 5, 20  # asinh

        width, height = 85, 75
        self.width = width
        self.height = height

        shape = (width, height)
        image_r = np.zeros(shape)
        image_g = np.zeros(shape)
        image_b = np.zeros(shape)

        # pixel locations, values and colors
        points = [[15, 15], [50, 45], [30, 30], [45, 15]]
        values = [1000, 5500, 600, 20000]
        g_r = [1.0, -1.0, 1.0, 1.0]
        r_i = [2.0, -0.5, 2.5, 1.0]

        # Put pixels in the images.
        for p, v, gr, ri in zip(points, values, g_r, r_i):
            image_r[p[0], p[1]] = v*pow(10, 0.4*ri)
            image_g[p[0], p[1]] = v*pow(10, 0.4*gr)
            image_b[p[0], p[1]] = v

        # convolve the image with a reasonable PSF, and add Gaussian background noise
        def convolve_with_noise(image, psf):
            convolvedImage = convolve(image, psf, boundary='extend', normalize_kernel=True)
            randomImage = np.random.normal(0, 2, image.shape)
            return randomImage + convolvedImage

        psf = Gaussian2DKernel(2.5)
        self.image_r = convolve_with_noise(image_r, psf)
        self.image_g = convolve_with_noise(image_g, psf)
        self.image_b = convolve_with_noise(image_b, psf) 

# 需要导入模块: from astropy import convolution [as 别名]
# 或者: from astropy.convolution import Gaussian2DKernel [as 别名]
def test_filter_2D(radius):
    data = np.random.rand(4, 3, 6)
    ds = xr.DataArray(data, dims=["x", "something", "y"])
    ds_filt = filter_2D(ds, radius, dim=["x", "y"])

    for xi, xx in enumerate(ds.something.data):
        sample = ds_filt.sel({"something": xx})
        kernel = Gaussian2DKernel(radius)
        expected = convolve_fft(data[:, xi, :], kernel, boundary="wrap")
        # result[np.isnan(raw_data)] = np.nan
        np.testing.assert_allclose(sample, expected)

    # TODO test case with nans (astropy interpolates nicely.
    # I want to make the nan substitution optiona) 

# 需要导入模块: from astropy import convolution [as 别名]
# 或者: from astropy.convolution import Gaussian2DKernel [as 别名]
def filter_2D(data, std, dim, dtype=None):
    if astropy is None:
        raise RuntimeError(
            "Module `astropy` not found. Please install optional dependency with `conda install -c conda-forge astropy"
        )

    if dtype is None:
        dtype = detect_dtype(data)

    kernel = Gaussian2DKernel(std)

    def smooth_raw(data):
        raw_data = getattr(data, "values", data)
        result = convolve_fft(raw_data, kernel, boundary="wrap")
        result[np.isnan(raw_data)] = np.nan
        return result

    def smoother(data):
        dims = dim
        # this is different from the 1d case

        return xr.apply_ufunc(
            smooth_raw,
            data,
            vectorize=True,
            dask="parallelized",
            input_core_dims=[dims],
            output_core_dims=[dims],
            output_dtypes=[dtype],
        )

    return smoother(data)


# TODO spatial filter 

# 需要导入模块: from astropy import convolution [as 别名]
# 或者: from astropy.convolution import Gaussian2DKernel [as 别名]
def frame_filter_lowpass(array, mode='gauss', median_size=5, fwhm_size=5,
                         gauss_mode='conv'):
    """
    Low-pass filtering of input frame depending on parameter ``mode``.

    Parameters
    ----------
    array : numpy ndarray
        Input array, 2d frame.
    mode : {'median', 'gauss'}, str optional
        Type of low-pass filtering.
    median_size : int, optional
        Size of the median box for filtering the low-pass median filter.
    fwhm_size : float, optional
        Size of the Gaussian kernel for the low-pass Gaussian filter.
    gauss_mode : {'conv', 'convfft'}, str optional
        'conv' uses the multidimensional gaussian filter from scipy.ndimage and
        'convfft' uses the fft convolution with a 2d Gaussian kernel.

    Returns
    -------
    filtered : numpy ndarray
        Low-pass filtered image.

    """
    if array.ndim != 2:
        raise TypeError('Input array is not a frame or 2d array.')
    if not isinstance(median_size, int):
        raise ValueError('`Median_size` must be integer')

    if mode == 'median':
        # creating the low_pass filtered (median) image
        filtered = median_filter(array, median_size, mode='nearest')
    elif mode == 'gauss':
        # 2d Gaussian filter
        sigma = fwhm_size * gaussian_fwhm_to_sigma
        if gauss_mode == 'conv':
            filtered = gaussian_filter(array, sigma=sigma, order=0,
                                       mode='nearest')
        elif gauss_mode == 'convfft':
            # FFT Convolution with a 2d gaussian kernel created with Astropy.
            filtered = convolve_fft(array, Gaussian2DKernel(stddev=sigma))
        else:
            raise TypeError('2d Gaussian filter mode not recognized')
    else:
        raise TypeError('Low-pass filter mode not recognized')

    return filtered