示例#1
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def mask_object(cube, decimate=False, remove_nan=False, **kwargs):
    obj_rmin = kwargs.get('obj_rmin', None)
    obj_rmax = kwargs.get('obj_rmax', None)
    if obj_rmin is not None or obj_rmax is not None:
        obj_mask = solar.define_map_mask(cube, **kwargs)
        # decimate is mandatory to remove nan because NaN * 0 = NaN
        if decimate or remove_nan:
            Mo = lo.decimate(obj_mask, dtype=cube.dtype)
        else:
            Mo = lo.ndmask(obj_mask, dtype=cube.dtype)
    return Mo, obj_mask
示例#2
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def stsrt(data, cube, **kwargs):
    """
    Smooth Temporal Solar Rotational Tomography.
    Assumes data is sorted by observation time 'DATE_OBS'.

    Returns
    -------
    P : The projector with masking
    D : Smoothness priors
    obj_mask : object mask array
    data_mask : data mask array
    """
    # Parse kwargs.
    obj_rmin = kwargs.get('obj_rmin', None)
    obj_rmax = kwargs.get('obj_rmax', None)
    # mask data
    data_mask = solar.define_data_mask(data, **kwargs)
    # define temporal groups
    times = [solar.convert_time(h['DATE_OBS']) for h in data.header]
    ## if no interval is given separate every image
    dt_min = kwargs.get('dt_min', np.max(np.diff(times)) + 1)
    #groups = solar.temporal_groups(data, dt_min)
    ind = solar.temporal_groups_indexes(data, dt_min)
    n = len(ind)
    # define new 4D cube
    cube4 = cube[..., np.newaxis].repeat(n, axis=-1)
    cube4.header = copy.copy(cube.header)
    cube4.header['NAXIS'] = 4
    cube4.header['NAXIS4'] = cube4.shape[3]
    # define 4d model
    # XXX assumes all groups have same number of elements
    ng = data.shape[-1] / n
    P = siddon4d_lo(data.header, cube4.header, ng=ng, mask=data_mask, obstacle="sun")
    # priors
    D = smoothness_prior(cube4, kwargs.get("height_prior", False))
    # mask object
    if obj_rmin is not None or obj_rmax is not None:
        Mo, obj_mask = mask_object(cube, **kwargs)
        obj_mask = obj_mask[..., np.newaxis].repeat(n, axis=-1)
        if kwargs.get("decimate", False) or kwargs.get("remove_nan", False):
            Mo = lo.decimate(obj_mask)
        else:
            Mo = lo.ndmask(obj_mask)
        P = P * Mo.T
        D = [Di * Mo.T for Di in D]
    else:
        obj_mask = None
    return P, D, obj_mask, data_mask
示例#3
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def check_model(model, im_h, obj_h):
    obj = siddon.simu.object_from_header(obj_h)
    obj = siddon.fa.InfoArray(data=obj, header=dict(obj.header))
    obj[:] = 1.
    im_h['n_images'] = 100
    im_h['max_lon'] = 3 * np.pi
    data = siddon.simu.circular_trajectory_data(**im_h)
    if obj.dtype == data.dtype:
        P, D, obj_mask, data_mask = model(data,
                                          obj,
                                          obj_rmin=1.,
                                          decimate=True)
        Mo = lo.decimate(obj_mask)
        w = (Mo.T * (P.T * np.ones(data.size))).reshape(obj_mask.shape)
        is_seen = (w != 0)
        new_obj = fa.FitsArray(obj_mask.shape)
        new_obj = 1.
        new_obj *= (1 - obj_mask)
        data[:] = (P * Mo * new_obj.ravel()).reshape(data.shape)
        hypers = new_obj.ndim * (1e-10, )
        sol = lo.acg(P, data.ravel(), D, hypers=hypers, tol=1e-20)
        sol = fa.asfitsarray((Mo.T * sol).reshape(obj_mask.shape),
                             header=obj.header)
        assert_almost_equal(sol[is_seen], new_obj[is_seen], decimal=1)
示例#4
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# define masking of coefficients
# ------------------------------
# number of coefficients to keep
factor = 8.
nk = P.shape[0] / factor
# coverage map
w = P.T * np.ones(P.shape[0])
# sort coverage map coefficients
ws = np.sort(w)
# find the nk largest coverage elements
threshold = ws[-nk]
mask = w < threshold
# define decimation Linear Operator according to coverage map coef
# selection.
M = lo.decimate(mask)
# model is projection matrix time decimation matrix
H = P * M.T

# Store model matrix on hard-drive as FITS
# ---------------------------------------------
# convert LinearOperator to dense matrix (ndarray)
Hd = H.todense()
Hd = np.asmatrix(Hd)
# convert into my FitsArray :
Hd_fits = fa.FitsArray(data=Hd)
# save the model to defined filename
filename = os.path.join(os.getenv("HOME"), "data", "pacs",
                        "mmc_model_cam_angle_0_scan_angle_0_speed60.fits")
Hd_fits.tofits(filename)
示例#5
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# define masking of coefficients
# ------------------------------
# number of coefficients to keep
factor = 8.
nk = P.shape[0] / factor
# coverage map
w = P.T * np.ones(P.shape[0])
# sort coverage map coefficients
ws = np.sort(w)
# find the nk largest coverage elements
threshold = ws[-nk]
mask = w < threshold
# define decimation Linear Operator according to coverage map coef
# selection.
M = lo.decimate(mask)
# model is projection matrix time decimation matrix
H = P * M.T

# Store model matrix on hard-drive as FITS
# ---------------------------------------------
# convert LinearOperator to dense matrix (ndarray)
Hd = H.todense()
Hd = np.asmatrix(Hd)
# convert into my FitsArray :
Hd_fits = fa.FitsArray(data=Hd)
# save the model to defined filename
filename = os.path.join(os.getenv("HOME"), "data", "pacs",
                        "mmc_model_cam_angle_0_scan_angle_0_speed60.fits")
Hd_fits.tofits(filename)