What parameters should I provide when calculating showalter_index？

[ShareRingNS]

When I try to calculate showalter_index？
`def showalter_index(pressure, temperature, dewpoint):
"""Calculate Showalter Index.

Showalter Index derived from [Galway1956]_:
SI = T500 - Tp500

where:
T500 is the measured temperature at 500 hPa
Tp500 is the temperature of the parcel at 500 hPa when lifted from 850 hPa

Parameters
----------
    pressure : `pint.Quantity`
        Atmospheric pressure, in order from highest to lowest pressure

    temperature : `pint.Quantity`
        Ambient temperature corresponding to ``pressure``

    dewpoint : `pint.Quantity`
        Ambient dew point temperatures corresponding to ``pressure``

Returns
-------
`pint.Quantity`
    Showalter index

"""
# find the measured temperature and dew point temperature at 850 hPa.
t850, td850 = interpolate_1d(units.Quantity(850, 'hPa'), pressure, temperature, dewpoint)

# find the parcel profile temperature at 500 hPa.
tp500 = interpolate_1d(units.Quantity(500, 'hPa'), pressure, temperature)

# Lift parcel from 850 to 500, handling any needed dry vs. saturated adiabatic processes
prof = parcel_profile(units.Quantity([850., 500.], 'hPa'), t850, td850)

# Calculate the Showalter index
return tp500 - prof[-1]

I try to input pressure_ 850_ 500 (2 * 151 * 151, fill 151 * 151 grid points with 850hpa and 500hpa respectively), t_ 850_ 500_ (2 * 151 * 151, temperature value at 850hpa and 500hpa on each grid point), dew_ 850_ (1 * 151 * 151, dew point temperature at 850hpa on each grid point)

t_850_500=multi_xr.t[12,[4,1],:,:]  #t:2,151,151

t_850_500_=units.Quantity(t_850_500.values,t.units)

q_850_500=multi_xr.q[12,[4,1],:,:]  #q:2,151,151

q_850_500_=units.Quantity(q_850_500.values,q.units)

pressure_850_500=lev[[0,2]].broadcast_like(q_850_500)  #pressure:2,151,151

dew_850_=mpcalc.dewpoint_from_specific_humidity(pressure_850_500,t_850_500_,q_850_500_).to_base_units()

s_i=mpcalc.showalter_index(pressure_850_500,t_850_500_,dew_850_[0])

But it got a bug

Traceback (most recent call last):
  File "C:/Users/Desktop/nudt_work/exp_doc/Toolbox/Index_Calculation2.py", line 141, in <module>

    s_i=mpcalc.showalter_index(pressure_850_500,t_850_500_,dew_850_500_[0])

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\xarray.py", line 1235, in wrapper

    result = func(*bound_args.args, **bound_args.kwargs)

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\units.py", line 296, in wrapper

    return func(*args, **kwargs)

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\calc\thermo.py", line 3507, in showalter_index

    t850, td850 = interpolate_1d(units.Quantity(850, 'hPa'), pressure, temperature, dewpoint)

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\xarray.py", line 1235, in wrapper

    result = func(*bound_args.args, **bound_args.kwargs)

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\interpolate\one_dimension.py", line 118, in interpolate_1d

    variables = [arr[sorter] for arr in args]

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\metpy\interpolate\one_dimension.py", line 118, in <listcomp>

    variables = [arr[sorter] for arr in args]

  File "C:\Users\anaconda3\envs\py38\lib\site-packages\pint\quantity.py", line 1903, in __getitem__

    return type(self)(self._magnitude[key], self._units)

IndexError: too many indices for array: array is 2-dimensional, but 3 were indexed

I tried to understand the source code, but failed, sorry to bother you.

[dopplershift]

Sorry, this got lost in the pile.

showalter_index, because it uses parcel_profile to calculate an appropriate parcel profile for you, is only able to operate on a single atmospheric profile at a time. You need to pass in the full profile of the atmosphere (or at least the surface up to 500 hPa).

If you have a grid of data, you'll need to iterate manually over the profiles within the grid and pass them one at a time. This is of course not great in Python and will incur a large performance penalty, but that's the only right now. We plan to address this limitation in the medium term with our performance-focused work.