Exemplo n.º 1
0
def survey_footprint(ax, survey_filename, FoV):
    ''' This function plots the survey footprint map on the given matplotlib
    axes instance

    Input
    -----
    ax                      :       matplotlib axes instance
        The axes instance to plot the footprint map to
    survey_filename         :       string
        The path to the survey file

    '''

    survey = np.loadtxt(survey_filename)

    x_min = -FoV[0] / 2
    y_min = -FoV[1] / 2
    x_max = FoV[0] / 2
    y_max = FoV[1] / 2
    x = np.array([x_min, x_min, x_max, x_max, x_min])
    y = np.array([y_min, y_max, y_max, y_min, y_min])

    for image in survey:
        alpha, beta = transformations.fp2sky(x, y, image[1:3], image[3])
        ax.plot(alpha, beta, 'k-', alpha=0.5)

    return None
Exemplo n.º 2
0
def survey_footprint(ax, survey_filename, FoV):
    ''' This function plots the survey footprint map on the given matplotlib
    axes instance

    Input
    -----
    ax                      :       matplotlib axes instance
        The axes instance to plot the footprint map to
    survey_filename         :       string
        The path to the survey file

    '''

    survey = np.loadtxt(survey_filename)

    x_min = -FoV[0] / 2
    y_min = -FoV[1] / 2
    x_max = FoV[0] / 2
    y_max = FoV[1] / 2
    x = np.array([x_min, x_min, x_max, x_max, x_min])
    y = np.array([y_min, y_max, y_max, y_min, y_min])

    for image in survey:
        alpha, beta = transformations.fp2sky(x, y, image[1:3], image[3])
        ax.plot(alpha, beta, 'k-', alpha=0.5)

    return None
Exemplo n.º 3
0
def camera(data_dir, sky_catalog, measured_catalog, FoV,
            pointing, orientation, verbose=False):
    ''' Saves out a single camera exposure, along with the total sky catalog
    and the meas

    Parameters
    ----------
    data_dir            :   string
        The output directory
    sky_catalog         :   object
        Object of the sky catalog (*.ID, *.mag, *.alpha, *.beta, *.size)
    measured_catalog    :   object
        Object of measured sources on focal plane in a single pointing
        (*.size, *.ID, *.x, *.y, *true_invvar, *.counts, *.invvar)
    FoV                 :   float array
        The imagers field-of-view in degrees (dalpha, dbeta)
    pointing            :   np.array
        Array of telescope pointing (alpha, beta)
    orientation         :   float
        Rotation of the telescope
    verbose             :   boolean
        True to run function in verbose mode
    '''

    filename = '{0}/camera_image.p'.format(data_dir)

    if verbose:
        print("Saving out camera image to {0}...".format(filename))

    x = 0.5 * np.array([-FoV[0], -FoV[0], FoV[0], FoV[0], -FoV[0]])
    y = 0.5 * np.array([-FoV[1], FoV[1], FoV[1], -FoV[1], -FoV[1]])
    alpha, beta = transformations.fp2sky(x, y, pointing, orientation)

    dic = {'sources_x': measured_catalog.x,
            'sources_y': measured_catalog.y,
            'sky_catalog': sky_catalog,
            'pointing': pointing,
            'orientation': orientation,
            'fp_x': x,
            'fp_y': y,
            'fp_alpha': alpha,
            'fp_beta': beta}
    pickle.dump(dic, open(filename, "wb"))

    if verbose:
        print("...done!")
fig.text(0.025, 0.5, r'Sky Position $\beta$ (deg$^2$)',
                                    va='center', ha='center', rotation=90)

for indx in range(len(dirs)):

    params = pickle.load(open('{0}/parameters.p'.format(dirs[indx])))
    FoV = params['FoV']
    sky_limits = params['sky_limits']

    x = 0.5 * np.array([-FoV[0], -FoV[0], FoV[0], FoV[0], -FoV[0]])
    y = 0.5 * np.array([-FoV[1], FoV[1], FoV[1], -FoV[1], -FoV[1]])

    survey = np.loadtxt((dirs[indx] + '/survey.txt'))

    for image in survey:
        alpha, beta = transformations.fp2sky(x, y, image[1:3], image[3])
        ax[indx].plot(alpha, beta, 'k-', alpha=0.4)

    min_sky = np.min(sky_limits) \
                        - 0.02 * (np.max(sky_limits) - np.min(sky_limits))
    max_sky = np.max(sky_limits) \
                        + 0.02 * (np.max(sky_limits) - np.min(sky_limits))
    ax[indx].set_xlim(min_sky, max_sky)
    ax[indx].set_ylim(min_sky, max_sky)

ax[0].text(0.95, 0.04, 'A', va='center', ha='center',
                                                    transform=ax[0].transAxes)
ax[1].text(0.05, 0.04, 'B', va='center', ha='center',
                                                    transform=ax[1].transAxes)
ax[2].text(0.95, 0.96, 'C', va='center', ha='center',
                                                    transform=ax[2].transAxes)