How to use the metpy.calc.parcel_profile function in MetPy

def test_cape_cin_no_el():
    """Test that CAPE works with no EL."""
    p = np.array([959., 779.2, 751.3, 724.3]) * units.mbar
    temperature = np.array([22.2, 14.6, 12., 9.4]) * units.celsius
    dewpoint = np.array([19., -11.2, -10.8, -10.4]) * units.celsius
    parcel_prof = parcel_profile(p, temperature[0], dewpoint[0]).to('degC')
    cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
    assert_almost_equal(cape, 0.08610409 * units('joule / kilogram'), 2)
    assert_almost_equal(cin, -89.8179205 * units('joule / kilogram'), 2)

def test_cape_cin_bottom_el_lfc(multiple_intersections):
    """Test using LFC/EL options for CAPE/CIN."""
    levels, temperatures, dewpoints = multiple_intersections
    parcel_prof = parcel_profile(levels, temperatures[0], dewpoints[0]).to('degC')
    cape, cin = cape_cin(levels, temperatures, dewpoints, parcel_prof, which_el='bottom')
    assert_almost_equal(cape, 2.1967 * units('joule / kilogram'), 3)
    assert_almost_equal(cin, -8.1545 * units('joule / kilogram'), 3)

727.9612, 704.1409, 680.4028, 656.7156, 629.077, 597.4286, 565.6315, 533.5961,
         501.2452, 468.493, 435.2486, 401.4239, 366.9387, 331.7026, 295.6319, 258.6428,
         220.9178, 182.9384, 144.959, 106.9778, 69.00213] * units.hPa
    t = [-3.039381, -3.703779, -4.15996, -4.562574, -5.131827, -5.856229, -6.568434,
         -7.276881, -7.985013, -8.670911, -8.958063, -7.631381, -6.05927, -5.083627,
         -5.11576, -5.687552, -5.453021, -4.981445, -5.236665, -6.324916, -8.434324,
         -11.58795, -14.99297, -18.45947, -21.92021, -25.40522, -28.914, -32.78637,
         -37.7179, -43.56836, -49.61077, -54.24449, -56.16666, -57.03775, -58.28041,
         -60.86264, -64.21677] * units.degC
    td = [-22.08774, -22.18181, -22.2508, -22.31323, -22.4024, -22.51582, -22.62526,
          -22.72919, -22.82095, -22.86173, -22.49489, -21.66936, -21.67332, -21.94054,
          -23.63561, -27.17466, -31.87395, -38.31725, -44.54717, -46.99218, -43.17544,
          -37.40019, -34.3351, -36.42896, -42.1396, -46.95909, -49.36232, -48.94634,
          -47.90178, -49.97902, -55.02753, -63.06276, -72.53742, -88.81377, -93.54573,
          -92.92464, -91.57479] * units.degC
    prof = parcel_profile(p, t[0], td[0]).to('degC')
    lfc_p, lfc_t = lfc(p, t, td, prof)
    assert_nan(lfc_p, p.units)
    assert_nan(lfc_t, t.units)

def test_cape_cin_custom_profile():
    """Test the CAPE and CIN calculation with a custom profile passed to LFC and EL."""
    p = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
    temperature = np.array([22.2, 14.6, 12., 9.4, 7., -38.]) * units.celsius
    dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
    parcel_prof = parcel_profile(p, temperature[0], dewpoint[0]) + 5 * units.delta_degC
    cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
    assert_almost_equal(cape, 1443.505086499895 * units('joule / kilogram'), 2)
    assert_almost_equal(cin, 0.0 * units('joule / kilogram'), 2)

def test_el_ml():
    """Test equilibrium layer calculation for a mixed parcel."""
    levels = np.array([959., 779.2, 751.3, 724.3, 700., 400., 269.]) * units.mbar
    temperatures = np.array([22.2, 14.6, 12., 9.4, 7., -25., -35.]) * units.celsius
    dewpoints = np.array([19., -11.2, -10.8, -10.4, -10., -35., -53.2]) * units.celsius
    __, t_mixed, td_mixed = mixed_parcel(levels, temperatures, dewpoints)
    mixed_parcel_prof = parcel_profile(levels, t_mixed, td_mixed)
    el_pressure, el_temperature = el(levels, temperatures, dewpoints, mixed_parcel_prof)
    assert_almost_equal(el_pressure, 349.919 * units.mbar, 3)
    assert_almost_equal(el_temperature, -28.371 * units.degC, 3)

skew = SkewT(fig, rotation=45)

    # Plot the data using normal plotting functions, in this case using
    # log scaling in Y, as dictated by the typical meteorological plot
    skew.plot(p, T, 'r')
    skew.plot(p, Td, 'g')
    skew.plot_barbs(p, u, v)
    skew.ax.set_ylim(1000, 100)
    skew.ax.set_xlim(-40, 60)

    # Calculate LCL height and plot as black dot
    lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
    skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')

    # Calculate full parcel profile and add to plot as black line
    prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
    skew.plot(p, prof, 'k', linewidth=2)

    # An example of a slanted line at constant T -- in this case the 0
    # isotherm
    skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)

    # Add the relevant special lines
    skew.plot_dry_adiabats()
    skew.plot_moist_adiabats()
    skew.plot_mixing_lines()

    return skew

skew = SkewT(fig, rotation=45)

# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)

# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')

# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)

# Shade areas of CAPE and CIN
skew.shade_cin(p, T, prof)
skew.shade_cape(p, T, prof)

# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)

# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()

# Show the plot

# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
#   relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning
#   at the surface temperature/pressure and rising dry adiabatically until
#   reaching the LCL, then rising moist adiabatially.

# Calculate the LCL
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])

print(lcl_pressure, lcl_temperature)

# Calculate the parcel profile.
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')

##########################################################################
# Basic Skew-T Plotting
# ---------------------
#
# The Skew-T (log-P) diagram is the standard way to view rawinsonde data. The
# y-axis is height in pressure coordinates and the x-axis is temperature. The
# y coordinates are plotted on a logarithmic scale and the x coordinate system
# is skewed. An explanation of skew-T interpretation is beyond the scope of this
# tutorial, but here we will plot one that can be used for analysis or
# publication.
#
# The most basic skew-T can be plotted with only five lines of Python.
# These lines perform the following tasks:
#
# 1. Create a ``Figure`` object and set the size of the figure.