How to use the pygmt.which function in pygmt

Parameters
    ----------
    resolution : str
        The grid resolution. The suffix ``m`` and ``s`` stand for arc-minute
        and arc-second. It can be ``'60m'``, ``'30m'``, ``'10m'``, ``'05m'``,
        ``'02m'``, ``'01m'``, ``'30s'`` or ``'15s'``.

    Returns
    -------
    grid : xarray.DataArray
        The Earth relief grid. Coordinates are latitude and longitude in
        degrees. Relief is in meters.

    """
    _is_valid_resolution(resolution)
    fname = which("@earth_relief_{}".format(resolution), download="u")
    grid = xr.open_dataarray(fname)
    # Add some metadata to the grid
    grid.name = "elevation"
    grid.attrs["long_name"] = "elevation relative to the geoid"
    grid.attrs["units"] = "meters"
    grid.attrs["vertical_datum"] = "EMG96"
    grid.attrs["horizontal_datum"] = "WGS84"
    # Remove the actual range because it gets outdated when indexing the grid,
    # which causes problems when exporting it to netCDF for usage on the
    # command-line.
    grid.attrs.pop("actual_range")
    for coord in grid.coords:
        grid[coord].attrs.pop("actual_range")
    return grid

Data is from the NOAA NGDC database. This is the ``@tut_quakes.ngdc``
    dataset used in the GMT tutorials.

    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the
    first time you invoke this function. Afterwards, it will load the data from
    the cache. So you'll need an internet connection the first time around.

    Returns
    -------
    data :  pandas.Dataframe
        The data table. Columns are year, month, day, latitude, longitude,
        depth (in km), and magnitude of the earthquakes.

    """
    fname = which("@tut_quakes.ngdc", download="c")
    data = pd.read_csv(fname, header=1, sep=r"\s+")
    data.columns = [
        "year",
        "month",
        "day",
        "latitude",
        "longitude",
        "depth_km",
        "magnitude",
    ]
    return data

Load a table of global earthquakes form the USGS as a pandas.Dataframe.

    This is the ``@usgs_quakes_22.txt`` dataset used in the GMT tutorials.

    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the
    first time you invoke this function. Afterwards, it will load the data from
    the cache. So you'll need an internet connection the first time around.

    Returns
    -------
    data : pandas.Dataframe
        The data table. Use ``print(data.describe())`` to see the available
        columns.

    """
    fname = which("@usgs_quakes_22.txt", download="c")
    data = pd.read_csv(fname)
    return data

"""
    Load a table of ship observations of bathymetry off Baja California as a
    pandas.DataFrame.

    This is the ``@tut_ship.xyz`` dataset used in the GMT tutorials.

    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the
    first time you invoke this function. Afterwards, it will load the data from
    the cache. So you'll need an internet connection the first time around.

    Returns
    -------
    data :  pandas.Dataframe
        The data table. Columns are longitude, latitude, and bathymetry.
    """
    fname = which("@tut_ship.xyz", download="c")
    data = pd.read_csv(
        fname, sep="\t", header=None, names=["longitude", "latitude", "bathymetry"]
    )
    return data