我正在用绘图中包含的一些复合参数绘制一些观察到的声音，但我对 STP 输出有一个小问题：它总是带有方括号。
我试图检查是否有统一的东西导致了这种情况，但我没有发现任何问题。
想法？
编辑：我很笨，忘记添加代码行
import posixpath
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
import matplotlib.gridspec as gridspec
import numpy as np
from metpy.plots import add_metpy_logo, add_timestamp, SkewT, Hodograph
from metpy.units import units
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from siphon.simplewebservice.wyoming import WyomingUpperAir
def plot_skewt(df):
# Pull the data out of the dataset and set them as individual variables and assign units.
height_AGL = (df['height']-df['elevation']).values * units.m
z = df['height'].values * units.m
p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
# Calculate LCL height and plot as black dot
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
lcl_hgt = np.round(mpcalc.pressure_to_height_std(lcl_pressure), decimals=3).to(units.meter)
lfc_hgt = np.round(mpcalc.pressure_to_height_std(lfc_pressure), decimals=3).to(units.meter)
# Calculate thermodynamic parameters
sb_cape, sb_cin = mpcalc.surface_based_cape_cin(p, T, Td)
ml_cape, ml_cin = mpcalc.mixed_layer_cape_cin(p, T, Td)
mu_cape, mu_cin = mpcalc.most_unstable_cape_cin(p, T, Td)
#lr_700_500 = np.round(-1 * np.divide(T[]-T[], (z[]-z[]).to(units.kilometer)),2)
sbcape = np.round(sb_cape, 1)
sbcin = np.round(sb_cin, 1)
mlcape = np.round(ml_cape, 1)
mlcin = np.round(ml_cin, 1)
mucape = np.round(mu_cape, 1)
# Calculate kinematic parameters
u_shear01, v_shear01 = mpcalc.bulk_shear(p, u.to(units('m/s')), v.to(units('m/s')), depth = 1000 * units.meter)
shear01 = np.round((np.sqrt(u_shear01**2 + v_shear01**2)), 1)
u_shear06, v_shear06 = mpcalc.bulk_shear(p, u.to(units('m/s')), v.to(units('m/s')), depth = 6000 * units.meter)
shear06 = np.round((np.sqrt(u_shear06**2 + v_shear06**2)), 1)
rmover, lmover, mean = mpcalc.bunkers_storm_motion(p, u, v, z)
srh_01_pos, srh_01_neg, srh_01_tot = mpcalc.storm_relative_helicity(u, v, z, depth = 1000 * units.meter, bottom = height_AGL[0], storm_u = lmover[0], storm_v = lmover[1])
srh_01 = np.round(srh_01_neg, 1)
srh_03_pos, srh_03_neg, srh_03_tot = mpcalc.storm_relative_helicity(u, v, z, depth = 3000 * units.meter, bottom = height_AGL[0], storm_u = lmover[0], storm_v = lmover[1])
srh_03 = np.round(srh_03_neg, 1)
# Calculate composite parameters
ehi_01 = np.round(np.divide(srh_01_neg * sb_cape, 160000 * ((units.m**2 * units.joule)/(units.s**2 * units.kilogram))), 1)
ehi_03 = np.round(np.divide(srh_03_neg * sb_cape, 160000 * ((units.m**2 * units.joule)/(units.s**2 * units.kilogram))), 1)
scp = np.round(np.divide(sb_cape, 1000 * units('J/kg')) * np.divide(shear06, 20 * units('m/s')) * np.divide(srh_03_neg, 100 * (units.m**2/units.s**2)), 1)
sig_tor = np.round((mpcalc.significant_tornado(sb_cape, lcl_hgt, srh_01_neg, shear06)), 1)
# Create a new axis
ax = plt.figure(figsize=(9,9))
# Grid for plots
gs = gridspec.GridSpec(3, 3)
skew = SkewT(ax, rotation=45, subplot=gs[:,:2])
# Plot the enviromental temperature and dewpoint profiles
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
# Mask barbs to below 100 hPa only
mask = p >= 100 * units.hPa
# Plot wind barbs
skew.plot_barbs(p[mask], u[mask], v[mask], y_clip_radius=0.01)
# Set axis limits
skew.ax.set_ylim(1050, 100)
skew.ax.set_xlim(-25, 40)
# 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)
# Mark with a dot the LCL and LFC heights
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
skew.plot(lfc_pressure, lfc_temperature, 'ko', markerfacecolor='blue')
# Mask barbs to below 100 hPa only
mask = p >= 100 * units.hPa
# Shade CAPE
skew.shade_cape(p, T, prof)
# Shade CIN
skew.shade_cin(p, T, prof)
# Plot the freezing layer isotherms
skew.ax.axvline(0, color='c', linestyle='--', linewidth=1)
skew.ax.axvline(-20, color='c', linestyle='--', linewidth=1)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Write parameters outputs in the SkewT
plt.figtext( 0.65, 0.58, 'LCL Height:')
plt.figtext( 0.8, 0.58, f'{lcl_hgt:~P}')
plt.figtext( 0.65, 0.56, 'LFC Height:')
plt.figtext( 0.8, 0.56, f'{lfc_hgt:~P}')
#plt.figtext( 0.65, 0.54, 'MLLR:')
#plt.figtext( 0.8, 0.54, f'{lr_700_500:~P}')
plt.figtext( 0.65, 0.54, 'SBCAPE:')
plt.figtext( 0.8, 0.54, f'{sbcape:~P}')
plt.figtext( 0.65, 0.52, 'SBCIN:')
plt.figtext( 0.8, 0.52, f'{sbcin:~P}')
plt.figtext( 0.65, 0.50, 'MLCAPE:')
plt.figtext( 0.8, 0.50, f'{mlcape:~P}')
plt.figtext( 0.65, 0.48, 'MLCIN:')
plt.figtext( 0.8, 0.48, f'{mlcin:~P}')
plt.figtext( 0.65, 0.46, 'MUCAPE:')
plt.figtext( 0.8, 0.46, f'{mucape:~P}')
plt.figtext( 0.65, 0.44, 'Shear 0-1 km:')
plt.figtext( 0.8, 0.44, f'{shear01:~P}')
plt.figtext( 0.65, 0.42, 'Shear 0-6 km:')
plt.figtext( 0.8, 0.42, f'{shear06:~P}')
plt.figtext( 0.65, 0.40, 'SRH 0-1 km:')
plt.figtext( 0.8, 0.40, f'{srh_01:~P}')
plt.figtext( 0.65, 0.38, 'SRH 0-3 km:')
plt.figtext( 0.8, 0.38, f'{srh_03:~P}')
plt.figtext( 0.65, 0.36, 'EHI 0-1 km:')
plt.figtext( 0.8, 0.36, f'{ehi_01:~P}')
plt.figtext( 0.65, 0.34, 'EHI 0-3 km:')
plt.figtext( 0.8, 0.34, f'{ehi_03:~P}')
plt.figtext( 0.65, 0.32, 'SCP:')
plt.figtext( 0.8, 0.32, f'{scp:~P}')
plt.figtext( 0.65, 0.30, 'SIGTOR (FL):')
plt.figtext( 0.8, 0.30, f'{sig_tor:~P}')
# Mask velocities to below 10 km only
mask = z <= 10*units.km
# Custom colorscale for the wind profile
intervals = np.array([0, 1, 3, 5, 10]) * units.km
colors = ['tab:red', 'tab:green', 'tab:blue', 'tab:olive']
# Create a hodograph
ax1 = ax.add_subplot(gs[0,-1])
h = Hodograph(ax1, component_range=40.)
h.add_grid(increment=5)
u1 = u.to(units('m/s'))
v1 = v.to(units('m/s'))
h.plot_colormapped(u1[mask], v1[mask], z[mask], intervals=intervals, colors=colors)
return skew