您提到想要绘制气体随时间的演变，但在您给出的代码示例中，您使用 wind_dir 作为 x 变量。在这个答案中，我忽略了这一点，而是使用 time 作为 x 变量。
查看您的代码，我了解到您想要创建两个由小倍数组成的不同数字，一个用于气体浓度，一个用于气体通量。对于这种绘图，我建议使用 pandas 或 seaborn，以便您可以一次绘制 pandas 数据框中包含的所有变量。这里我分享一个使用 pandas 的例子。
因为您想要绘制相同物质的不同测量值，我建议创建一个表格，列出与每种独特物质相关的变量和单位的名称（参见下面的df_subs）。我创建了一个使用代码来提取单位并在此处共享它，但使用电子表格软件更容易做到这一点。
拥有这样的表格可以更轻松地创建一个绘图函数，该函数可以从 ec_top 数据框中选择要绘制的变量组。然后，您可以像这样使用 pandas 绘图功能：df.plot(subplots=True)。
下面显示的大部分代码都是根据您的代码创建一些示例数据，以便您可以准确地重新创建我在此处显示的内容，以及任何想尝试的人。因此，如果您想使用此解决方案，您可以跳过大部分内容，您只需按照自己的方式创建物质表，然后调整绘图功能以适应您的偏好。
创建示例数据集
import io # from Python v 3.8.5
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
import matplotlib.dates as mdates
pd.set_option("display.max_columns", 6)
rng = np.random.default_rng(seed=1) # random number generator
# Copy paste variable names from sample given in question
var_strings = '''
"M 33(ppbv)"
"M 39(ncps)"
"M 45(ppbv)"
"M 59(ppbv)"
"M 69(ppbv)"
"M 71(ppbv)"
"M 81(ppbv)"
"M 137(ppbv)"
"M 87(ppbv)"
"M 47(ppbv)"
"M 61(ppbv)"
"Flux_M 33"
"Flux_M 45"
"Flux_M 59"
"Flux_M 69"
"Flux_M 71"
"Flux_M 81"
"Flux_M 137"
"Flux_M 87"
"Flux_M 47"
"Flux_M 61"
'''
variables = pd.read_csv(io.StringIO(var_strings), header=None, names=['var'])['var']
# Create datetime variable
nperiods = 60
time = pd.date_range('2021-01-15 12:00', periods=nperiods, freq='min')
# Create range of numbers to compute sine waves for fake data
x = np.linspace(0, 2*np.pi, nperiods)
# Create dataframe containing gas concentrations
var_conc = np.array([var for var in variables if '(' in var])
conc_sine_wave = np.reshape(np.sin(x), (len(x), 1))
loc = rng.exponential(scale=10, size=var_conc.size)
scale = loc/10
var_conc_noise = rng.normal(loc, scale, size=(x.size, var_conc.size))
data_conc = conc_sine_wave + var_conc_noise + 2
df_conc = pd.DataFrame(data_conc, index=time, columns=var_conc)
# Create dataframe containing gas fluxes
var_flux = np.array([var for var in variables if 'Flux' in var])
flux_sine_wave = np.reshape(np.sin(x)**2, (len(x), 1))
loc = rng.exponential(scale=10, size=var_flux.size)
scale = loc/10
var_flux_noise = rng.normal(loc, scale, size=(x.size, var_flux.size))
data_flux = flux_sine_wave + var_flux_noise + 1
df_flux = pd.DataFrame(data_flux, index=time, columns=var_flux)
# Merge concentrations and fluxes into single dataframe
ec_top = pd.merge(left=df_conc, right=df_flux, how='outer',
left_index=True, right_index=True)
ec_top.head()
# M 33(ppbv) M 39(ncps) M 45(ppbv) ... Flux_M 87 Flux_M 47 Flux_M 61
# 2021-01-15 12:00:00 11.940054 5.034281 53.162767 ... 8.079255 2.402073 31.383911
# 2021-01-15 12:01:00 13.916828 4.354558 45.706391 ... 10.229084 2.494649 26.816754
# 2021-01-15 12:02:00 13.635604 5.500438 53.202743 ... 12.772899 2.441369 33.219213
# 2021-01-15 12:03:00 13.146823 5.409585 53.346907 ... 11.373669 2.817323 33.409331
# 2021-01-15 12:04:00 14.124752 5.491555 49.455010 ... 11.827497 2.939942 28.639749
创建包含变量名称和单位的物质表
这些物质按它们在此处列出的顺序显示在图中的子图中。该表中的信息用于创建子图的标签和标题。
# Copy paste substance codes and names from sample given in question
subs_strings = """
M33 "Methanol"
M39 "Water cluster"
M45 "Acetaldehyde"
M47 "Unknown"
M59 "Acetone"
M61 "Unknown"
M69 "Isoprene"
M71 "Methyl vinyl, ketone and methacrolein"
M81 "Fragment of monoterpenes"
M87 "Methylbutenols"
M137 "Monoterpenes"
"""
# Create dataframe containing substance codes and names
df_subs = pd.read_csv(io.StringIO(subs_strings), header=None,
names=['subs', 'subs_name'], index_col=False,
delim_whitespace=True)
# Add units and variable names matching the substance codes
# Do this for gas concentrations
for var in var_conc:
var_subs, var_unit_raw = var.split('(')
var_subs_num = var_subs.lstrip('M ')
var_unit = var_unit_raw.rstrip(')')
for i, subs in enumerate(df_subs['subs']):
if var_subs_num == subs.lstrip('M'):
df_subs.loc[i, 'conc_unit'] = var_unit
df_subs.loc[i, 'conc_var'] = var
# Do this for gas fluxes
for var in var_flux:
var_subs_num = var.split('M')[1].lstrip()
var_unit = rng.choice(['unit_a', 'unit_b', 'unit_c'])
for i, subs in enumerate(df_subs['subs']):
if var_subs_num == subs.lstrip('M'):
df_subs.loc[i, 'flux_unit'] = var_unit
df_subs.loc[i, 'flux_var'] = var
df_subs
# subs subs_name conc_unit conc_var flux_unit flux_var
# 0 M33 Methanol ppbv M 33(ppbv) unit_c Flux_M 33
# 1 M39 Water cluster ncps M 39(ncps) NaN NaN
# 2 M45 Acetaldehyde ppbv M 45(ppbv) unit_a Flux_M 45
# 3 M47 Unknown ppbv M 47(ppbv) unit_b Flux_M 47
# 4 M59 Acetone ppbv M 59(ppbv) unit_a Flux_M 59
# 5 M61 Unknown ppbv M 61(ppbv) unit_c Flux_M 61
# 6 M69 Isoprene ppbv M 69(ppbv) unit_a Flux_M 69
# 7 M71 Methyl vinyl, ketone and methacrolein ppbv M 71(ppbv) unit_a Flux_M 71
# 8 M81 Fragment of monoterpenes ppbv M 81(ppbv) unit_c Flux_M 81
# 9 M87 Methylbutenols ppbv M 87(ppbv) unit_c Flux_M 87
# 10 M137 Monoterpenes ppbv M 137(ppbv) unit_b Flux_M 137
基于 pandas 创建绘图函数
这是创建绘图函数的一种方法，可让您使用 graph_type 参数为绘图选择变量。它通过使用if/elif 语句从物质表中选择相关变量来工作。这和ec_top[variables].plot(...) 函数是创建绘图所必需的，其余的都是用于格式化图形。变量按variables 列表的顺序绘制。由于此处的宽度限制，我只绘制了两列子图（最大 10 英寸宽度，以便在 Stack Overflow 上获得清晰的图像）。
# Create plotting function that creates a single figure showing all
# variables of the chosen type
def plot_grid(graph_type):
# Set the type of variables and units to fetch in df_subs: using if
# statements for the strings lets you use a variety of strings
if 'conc' in graph_type:
var_type = 'conc_var'
unit_type = 'conc_unit'
elif 'flux' in graph_type:
var_type = 'flux_var'
unit_type = 'flux_unit'
else:
return f'Error: "{graph_type}" is not a valid string, \
it must contain "conc" or "flux".'
# Create list of variables to plot depending on type
variables = df_subs[var_type].dropna()
# Set parameters for figure dimensions
nvar = variables.size
cols = 2
rows = int(np.ceil(nvar/cols))
width = 10/cols
height = 3
# Draw grid of line plots: note that x_compat is used to override the
# default x-axis time labels, remove it if you do not want to use custom
# tick locators and formatters like the ones created in the loop below
grid = ec_top[variables].plot(subplots=True, figsize=(cols*width, rows*height),
layout=(rows, cols), marker='.', linestyle='',
xlabel='Time', x_compat=True)
# The code in the following loop is optional formatting based on my
# preferences, if you remove it the plot should still look ok but with
# fewer informative labels and the legends may not all be in the same place
# Loop through the subplots to edit format, including creating labels and
# titles based on the information in the substances table (df_subs):
for ax in grid.flatten()[:nvar]:
# Edit tick locations and format
plt.setp(ax.get_xticklabels(which='both'), fontsize=8, rotation=0, ha='center')
loc = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(loc)
ax.set_xticks([], minor=True)
fmt = mdates.ConciseDateFormatter(loc, show_offset=False)
ax.xaxis.set_major_formatter(fmt)
# Edit legend
handle, (var_name,) = ax.get_legend_handles_labels()
subs = df_subs[df_subs[var_type] == var_name]['subs']
ax.legend(handle, subs, loc='upper right')
# Add y label
var_unit, = df_subs[df_subs[var_type] == var_name][unit_type]
ylabel_type = f'{"Concentration" if "conc" in graph_type else "Flux"}'
ax.set_ylabel(f'{ylabel_type} [{var_unit}]')
# Add title
subs_name, = df_subs[df_subs[var_type] == var_name]['subs_name']
ax.set_title(subs_name)
# Edit figure format
fig = plt.gcf()
date = df_conc.index[0].strftime('%b %d %Y')
title_type = f'{"concentrations" if "conc" in graph_type else "fluxes"}'
fig.suptitle(f'BVOCs {title_type} on {date} from 12:00 to 13:00',
y=0.93, fontsize=15);
fig.subplots_adjust(wspace=0.3, hspace=0.4)
plt.show()
plot_grid('conc') # any kind of string works if it contains 'conc' or 'flux'
plot_grid('graph fluxes')
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