Skip to content

redplanet.Crust.dichotomy.is_above 

is_above(
    lon: float | numpy.ndarray,
    lat: float | numpy.ndarray,
    as_xarray: bool = False,
) -> bool | numpy.ndarray | xarray.core.dataarray.DataArray

Determine if the given point(s) are above the dichotomy boundary.

See help(redplanet.Crust.dichotomy.get_coords) for the source of the dichotomy boundary coordinates data.

Parameters:

Name Type Description Default
lon float | np.ndarray

Longitude coordinate(s) in range [-180, 360].

 required
lat float | np.ndarray

Latitude coordinate(s) in range [-90, 90].

 required
as_xarray bool

If True, return the data as an xarray.DataArray. Default is False.

 False

Returns:

Type Description
bool | np.ndarray | xr.DataArray

Boolean array indicating whether the input coordinates are above the dichotomy boundary. If both inputs are 1D numpy arrays then it returns a 2D numpy array with shape (len(lat), len(lon)).
Source code in src/redplanet/Crust/dichotomy.py 
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
@substitute_docstrings
def is_above(
    lon       : float | np.ndarray,
    lat       : float | np.ndarray,
    as_xarray : bool = False,
) -> bool | np.ndarray | xr.DataArray:
    """
    Determine if the given point(s) are above the dichotomy boundary.

    See `help(redplanet.Crust.dichotomy.get_coords)` for the source of the dichotomy boundary coordinates data.

    Parameters
    ----------
    {param.lon}
    {param.lat}
    {param.as_xarray}

    Returns
    -------
    bool | np.ndarray | xr.DataArray
        Boolean array indicating whether the input coordinates are above the dichotomy boundary. If both inputs are 1D numpy arrays then it returns a 2D numpy array with shape `(len(lat), len(lon))`.
    """

    ## input validation
    _verify_coords(lon, lat)
    lon = _slon2plon(lon)

    lon = np.atleast_1d(lon)
    lat = np.atleast_1d(lat)

    ## load dataset
    dat_dichotomy_coords = get_coords()

    ## for each input longitude, find nearest dichotomy coordinates
    i_lons = np.searchsorted(dat_dichotomy_coords[:,0], lon, side='right') - 1
    llons, llats = dat_dichotomy_coords[i_lons].T
    rlons, rlats = dat_dichotomy_coords[i_lons+1].T

    ## linear interpolate between two nearest dichotomy coordinates to find threshold latitude
    tlats = llats + (rlats - llats) * ( (lon - llons) / (rlons - llons) )

    ## compare shape(y,1) with shape(x), which broadcasts to shape(y,x) with element-wise comparison
    result = lat[:, None] >= tlats

    ## remove singleton arrays/dimensions (i.e. one or both inputs were scalars)
    result = np.squeeze(result)
    if result.size == 1:
        return result.item()

    elif as_xarray:
        result = xr.DataArray(
            result,
            dims   = ("lat", "lon"),
            coords = {"lat": lat, "lon": lon},
        )
        # result = result.sortby('lat').sortby('lon')
    return result