diagnostic_plots.py 文件源码

def densityPlot(targ_ra, targ_dec, data, iso, g_radius, nbhd, type):
    """Stellar density plot"""

    mag_g = data[mag_g_dred_flag]
    mag_r = data[mag_r_dred_flag]

    if type == 'stars':
        filter = star_filter(data)
        plt.title('Stellar Density')
    elif type == 'galaxies':
        filter = galaxy_filter(data)
        plt.title('Galactic Density')
    elif type == 'blue_stars':
        filter = blue_star_filter(data)
        plt.title('Blue Stellar Density')

    iso_filter = (iso.separation(mag_g, mag_r) < 0.1)

    # projection of image
    proj = ugali.utils.projector.Projector(targ_ra, targ_dec)
    x, y = proj.sphereToImage(data[filter & iso_filter]['RA'], data[filter & iso_filter]['DEC']) # filter & iso_filter

    bound = 0.5 #1.
    steps = 100.
    bins = np.linspace(-bound, bound, steps)

    signal = np.histogram2d(x, y, bins=[bins, bins])[0]

    sigma = 0.01 * (0.25 * np.arctan(0.25*g_radius*60. - 1.5) + 1.3) # full range, arctan

    convolution = scipy.ndimage.filters.gaussian_filter(signal, sigma/(bound/steps))
    plt.pcolormesh(bins, bins, convolution.T, cmap='Greys')

    plt.xlim(bound, -bound)
    plt.ylim(-bound, bound)
    plt.gca().set_aspect('equal')
    plt.xlabel(r'$\Delta \alpha$ (deg)')
    plt.ylabel(r'$\Delta \delta$ (deg)')

    ax = plt.gca()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes('right', size = '5%', pad=0)
    plt.colorbar(cax=cax)