How to use the metpy.cbook.get_test_data function in MetPy

def test_declarative_barb_earth_relative():
    """Test making a contour plot."""
    import numpy as np
    data = xr.open_dataset(get_test_data('NAM_test.nc', as_file_obj=False))

    contour = ContourPlot()
    contour.data = data
    contour.field = 'Geopotential_height_isobaric'
    contour.level = 300 * units.hPa
    contour.linecolor = 'red'
    contour.linestyle = '-'
    contour.linewidth = 2
    contour.contours = np.arange(0, 20000, 120).tolist()

    barb = BarbPlot()
    barb.data = data
    barb.level = 300 * units.hPa
    barb.time = datetime(2016, 10, 31, 12)
    barb.field = ['u-component_of_wind_isobaric', 'v-component_of_wind_isobaric']
    barb.skip = (5, 5)

def test_declarative_contour_options():
    """Test making a contour plot."""
    data = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))

    contour = ContourPlot()
    contour.data = data
    contour.field = 'Temperature'
    contour.level = 700 * units.hPa
    contour.contours = 30
    contour.linewidth = 1
    contour.linecolor = 'red'
    contour.linestyle = 'dashed'
    contour.clabels = True

    panel = MapPanel()
    panel.area = 'us'
    panel.proj = 'lcc'
    panel.layers = ['coastline', 'borders', 'usstates']
    panel.plots = [contour]

def test_declarative_sfc_obs():
    """Test making a surface observation plot."""
    data = pd.read_csv(get_test_data('SFC_obs.csv', as_file_obj=False),
                       infer_datetime_format=True, parse_dates=['valid'])

    obs = PlotObs()
    obs.data = data
    obs.time = datetime(1993, 3, 12, 12)
    obs.time_window = timedelta(minutes=15)
    obs.level = None
    obs.fields = ['tmpf']
    obs.color = ['black']

    # Panel for plot with Map features
    panel = MapPanel()
    panel.layout = (1, 1, 1)
    panel.projection = ccrs.PlateCarree()
    panel.area = 'in'
    panel.layers = ['states']

extra_kw = {}
    if method == 'cressman':
        extra_kw['search_radius'] = 200
        extra_kw['minimum_neighbors'] = 1
    elif method == 'barnes':
        extra_kw['search_radius'] = 400
        extra_kw['minimum_neighbors'] = 1
        extra_kw['gamma'] = 1

    if boundary_coords is not None:
        extra_kw['boundary_coords'] = boundary_coords

    with pytest.warns(MetpyDeprecationWarning):
        _, _, img = interpolate(xp, yp, z, hres=10, interp_type=method, **extra_kw)

    with get_test_data('{0}_test.npz'.format(method)) as fobj:
        truth = np.load(fobj)['img']

    assert_array_almost_equal(truth, img)

def test_grid():
    r"""Return grid locations used for tests in this file."""
    with get_test_data('interpolation_test_grid.npz') as fobj:
        data = np.load(fobj)
        return data['xg'], data['yg']

xg, yg = test_grid

    extra_kw = {}
    if method == 'cressman':
        extra_kw['r'] = 20
        extra_kw['min_neighbors'] = 1
        test_file = 'cressman_r20_mn1.npz'
    elif method == 'barnes':
        extra_kw['r'] = 40
        extra_kw['kappa'] = 100
        test_file = 'barnes_r40_k100.npz'

    with pytest.warns(MetpyDeprecationWarning):
        img = inverse_distance(xp, yp, z, xg, yg, kind=method, **extra_kw)

    with get_test_data(test_file) as fobj:
        truth = np.load(fobj)['img']

    assert_array_almost_equal(truth, img)

def test_declarative_upa_obs():
    """Test making a full upperair observation plot."""
    data = pd.read_csv(get_test_data('UPA_obs.csv', as_file_obj=False))

    obs = PlotObs()
    obs.data = data
    obs.time = datetime(1993, 3, 14, 0)
    obs.level = 500 * units.hPa
    obs.fields = ['temperature', 'dewpoint', 'height']
    obs.locations = ['NW', 'SW', 'NE']
    obs.formats = [None, None, lambda v: format(v, '.0f')[:3]]
    obs.vector_field = ('u_wind', 'v_wind')
    obs.reduce_points = 0

    # Panel for plot with Map features
    panel = MapPanel()
    panel.layout = (1, 1, 1)
    panel.area = (-124, -72, 20, 53)
    panel.projection = 'lcc'

def test_msg15():
    """Check proper decoding of message type 15."""
    f = Level2File(get_test_data('KTLX20130520_201643_V06.gz', as_file_obj=False))
    data = f.clutter_filter_map['data']
    assert isinstance(data[0][0], list)
    assert f.clutter_filter_map['datetime'] == datetime(2013, 5, 19, 0, 0, 0, 315000)

import xarray as xr

import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.interpolate import cross_section

##############################
# **Getting the data**
#
# This example uses NARR reanalysis data for 18 UTC 04 April 1987 from NCEI
# (https://www.ncdc.noaa.gov/data-access/model-data).
#
# We use MetPy's CF parsing to get the data ready for use, and squeeze down the size-one time
# dimension.

data = xr.open_dataset(get_test_data('narr_example.nc', False))
data = data.metpy.parse_cf().squeeze()
print(data)

##############################
# Define start and end points:

start = (37.0, -105.0)
end = (35.5, -65.0)

##############################
# Get the cross section, and convert lat/lon to supplementary coordinates:

cross = cross_section(data, start, end).set_coords(('lat', 'lon'))
print(cross)

##############################

def station_test_data(variable_names, proj_from=None, proj_to=None):
    with get_test_data('station_data.txt') as f:
        all_data = np.loadtxt(f, skiprows=1, delimiter=',',
                              usecols=(1, 2, 3, 4, 5, 6, 7, 17, 18, 19),
                              dtype=np.dtype([('stid', '3S'), ('lat', 'f'), ('lon', 'f'),
                                              ('slp', 'f'), ('air_temperature', 'f'),
                                              ('cloud_fraction', 'f'), ('dewpoint', 'f'),
                                              ('weather', '16S'),
                                              ('wind_dir', 'f'), ('wind_speed', 'f')]))

    all_stids = [s.decode('ascii') for s in all_data['stid']]

    data = np.concatenate([all_data[all_stids.index(site)].reshape(1, ) for site in all_stids])

    value = data[variable_names]
    lon = data['lon']
    lat = data['lat']