我正在寻找一种方法来消除子情节之间的垂直空间。我尝试使用fig.subplots_adjust(hspace=0)，但似乎没有效果。有人知道如何做到这一点吗？
我将在下面添加我正在使用的代码（尽管它可能有点长），但是如果你想尝试自己运行它，这里有一个到我的github的链接，其中包含jupyter代码和我正在使用的文件
https://github.com/FrancescoAzzollini/Langmuir-Waves-Analysis
先谢谢你的帮助。

import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import colors
import pandas as pd
import matplotlib.dates as mdates
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import LogNorm
from matplotlib.pyplot import cm

event_start_time = dt.datetime(2002,4,25,5,56,6) # start time of the event
f2_header = 46 # 6 eV to 1113 eV 
f3_header = 51 # 200 eV to 27 keV 
f1_header = 72 # 27 keV up 
spec_header = 43

date_for_path = '25-04-2002'
path = 'Langmuir waves analysis/' + date_for_path + '/'
spec_name = 'WI_H1_WAV_23030.csv'
f2_name = 'WI_ELSP_3DP_23030.csv' # ELSP
f3_name = 'WI_EHSP_3DP_23030.csv' # EHSP
f1_name = 'WI_SFSP_3DP_23030.csv' # SFSP

def arrival_time(E):
    m = 9.1e-31
    v = np.sqrt(3.2e-16 * E/m)
    dist = 215*7e8
    t = dist/v
    return event_start_time + dt.timedelta(0,t) 

df_spec = pd.read_csv(path + spec_name, header=spec_header, skipfooter=3)
df_spec['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'] = pd.to_datetime(df_spec['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'], format = '%Y-%m-%dT%H:%M:%S.%fZ')

dates_list = df_spec['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'].to_list()
x_lims_ = [dates_list[0].timestamp(), dates_list[-1].timestamp()]
x_lims = list(map(dt.datetime.fromtimestamp, x_lims_))
x_lims = mdates.date2num(x_lims)

y_lims0 = np.linspace(1075, 13825, 100)
y_lims1 = np.linspace(20, 1040, 100)
y_lims2 = np.linspace(4, 245, 100)

@plt.FuncFormatter
def fake_log(x, pos):
    'The two args are the value and tick position'
    return round(10**x) #r'$10^{%.2f}$' % (x)

df_spec = df_spec.set_index('EPOCH_yyyy-mm-ddThh:mm:ss.sssZ')
df_spec = df_spec[df_spec.columns[::-1]].T

df_spec0 = df_spec.iloc[353:-1, :]
df_spec1 = df_spec.iloc[95:353, :]
df_spec2 = df_spec.iloc[0:95, :]

date_format = mdates.DateFormatter('%H:%M:%S')
locator = mdates.HourLocator([4, 5, 6, 7, 8, 9])

fig, ax = plt.subplots(6, 1, figsize=(15,20), layout='compressed')

col_posit = -0.1
flare_time = dt.datetime.fromtimestamp(event_start_time.timestamp() + 3600)
flare_time = mdates.date2num(flare_time)

for a in ax:
    a.set_anchor('W')

for a in ax:
    a.patch.set_facecolor('black')

im = ax[0].imshow(df_spec0, norm=colors.LogNorm(), cmap=cm.inferno,
                  extent = [x_lims[0], x_lims[1],  np.log10(y_lims0[0]), np.log10(y_lims0[-1])], aspect='auto')
fig.colorbar(im, ax=ax[0], pad=col_posit)

ax[0].xaxis.set(major_formatter=date_format)
ax[0].tick_params(bottom = False)
ax[0].set_xticks([])
ax[0].yaxis.set_major_formatter(fake_log)
ax[0].yaxis.set_major_locator(MaxNLocator(5)) 
ax[0].set_yticks(np.log10(np.array([2000, 5000, 10000])))

im = ax[1].imshow(df_spec1, norm=colors.LogNorm(), cmap=cm.inferno,
                  extent = [x_lims[0], x_lims[1],  np.log10(y_lims1[0]), np.log10(y_lims1[-1])], aspect='auto')
fig.colorbar(im, ax=ax[1], pad=col_posit)

ax[1].xaxis.set(major_formatter=date_format)
ax[1].tick_params(bottom = False)
ax[1].set_xticks([])
ax[1].yaxis.set_major_formatter(fake_log)
ax[1].yaxis.set_major_locator(MaxNLocator(5)) 
ax[1].set_yticks(np.log10(np.array([20, 50, 100, 200, 500, 1000])))

im = ax[2].imshow(df_spec2, norm=colors.LogNorm(), cmap=cm.inferno, 
                  extent = [x_lims[0], x_lims[1],  np.log10(y_lims2[0]), np.log10(y_lims2[-1])], aspect='auto')
fig.colorbar(im, ax=ax[2], pad=col_posit)

ax[2].xaxis.set(major_formatter=date_format)
ax[2].tick_params(bottom = False)
ax[2].set_xticks([])
ax[2].yaxis.set_major_formatter(fake_log)
ax[2].yaxis.set_major_locator(MaxNLocator(5)) 
ax[2].set_yticks(np.log10(np.array([10, 20, 50, 100, 200])))

#second set of images

df1 = pd.read_csv(path + f1_name, header=f1_header, skipfooter=3)
df1['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'] = pd.to_datetime(df1['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'], format = '%Y-%m-%dT%H:%M:%S.%fZ')

df2 = pd.read_csv(path + f2_name, header=f2_header, skipfooter=3)
df2['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'] = pd.to_datetime(df2['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'], format = '%Y-%m-%dT%H:%M:%S.%fZ')

df3 = pd.read_csv(path + f3_name, header=f3_header, skipfooter=3)
df3['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'] = pd.to_datetime(df3['EPOCH_yyyy-mm-ddThh:mm:ss.sssZ'], format = '%Y-%m-%dT%H:%M:%S.%fZ')

i1, i2, i3 = 1, 1, 1
energies1 = [27, 40, 66, 108, 181, 310, 517]
energies2 = list(reversed(list(np.array([6, 7, 9, 13, 18, 27, 41, 65, 103, 164, 264, 426, 689, 1113])*1e-3)))
energies3 = list(reversed([0.137, 0.2, 0.29, 0.43, 0.64, 0.92, 1.34, 1.95, 2.85, 4.16, 6.08, 6.87, 12.97, 18.9, 27]))

#fig, ax = plt.subplots(3, 1, figsize=(10,10))
while i1 < 8 :
    ax[5].plot(df1.iloc[:, 0], df1.iloc[:, i1], c=cm.rainbow([50*i1]))
    ax[5].vlines(arrival_time(energies1[i1-1]), 0,0.3, color=cm.rainbow([50*i1]), label = str(energies1[i1-1]) + ' keV')
    ax[5].set_xlim(df3.iloc[0, 0], df3.iloc[-1, 0])
    ax[5].set_yscale("log")
    plt.gcf().autofmt_xdate()
    i1 += 1

while i2 < 15 :
    ax[3].plot(df2.iloc[:, 0], df2.iloc[:, i2], c=cm.rainbow([20*i2]), label = str(round(energies2[i2-1]*1e3)) + ' eV')
    ax[3].vlines(arrival_time(energies2[i2-1]), 0,4e8, color=cm.rainbow([20*i2]))
    ax[3].set_xlim(df3.iloc[0, 0], df3.iloc[-1, 0])
    ax[3].set_yscale("log")
    i2 += 1

while i3 < 15 :
    ax[4].plot(df3.iloc[:, 0], df3.iloc[:, i3], c=cm.rainbow([20*i3]), label = str(round(energies3[i3-1]*1e3)) + ' eV')
    ax[4].vlines(arrival_time(energies3[i3-1]), 0,1e5, color=cm.rainbow([20*i3]))
    ax[4].set_xlim(df3.iloc[0, 0], df3.iloc[-1, 0])
    ax[4].set_yscale("log")
    i3 += 1

ax[0].vlines(flare_time, np.log10(y_lims0[0]), np.log10(y_lims0[-1]), 'r')
ax[1].vlines(flare_time, np.log10(y_lims1[0]), np.log10(y_lims1[-1]), 'r')
ax[2].vlines(flare_time, np.log10(y_lims2[0]), np.log10(y_lims2[-1]), 'r')
ax[4].set_ylim(None, 8e4)
ax[3].set_ylim(top=3e8)
ax[5].set_ylim(top=0.2)
ax[3].set_xticks([])
ax[4].set_xticks([])
ax[3].legend(loc='upper left', bbox_to_anchor=(1, 1),fontsize = 11, frameon=False)
ax[4].legend(loc='upper left', bbox_to_anchor=(1, 1),fontsize = 11, frameon=False)
ax[5].legend(loc='upper left', bbox_to_anchor=(1, 1),fontsize = 11, frameon=False)
ax[5].set_xlabel('UT', fontsize = 20)
ax[1].set_ylabel('Frequency [kHz]', fontsize = 20)
ax[4].set_ylabel('Eletrons $cm^{-2} ster^{-1} s^{-1} eV^{-1}$', fontsize = 20)
plt.rc('ytick', labelsize=16)
plt.rc('xtick', labelsize=16)

#plt.show()
plt.savefig(path + "Spec_Plot_" + date_for_path, bbox_inches='tight')