python - 如何使用动态变量创建图
问题描述
在 Pyhton 上使用 matplotlib 库，我想绘制一些带有动态 y 变量的图形，即会根据我的绘图函数之前声明的另一个变量而改变的变量。
从我导入的数据框中，我提取了不同的气体浓度 (M**_conc) 和通量 (M**_fluxes)。
M33_conc = ec_top["M 33(ppbv)"]
M39_conc = ec_top["M 39(ncps)"]
M45_conc = ec_top["M 45(ppbv)"]
M59_conc = ec_top["M 59(ppbv)"]
M69_conc = ec_top["M 69(ppbv)"]
M71_conc = ec_top["M 71(ppbv)"]
M81_conc = ec_top["M 81(ppbv)"]
M137_conc = ec_top["M 137(ppbv)"]
M87_conc = ec_top["M 87(ppbv)"]
M47_conc = ec_top["M 47(ppbv)"]
M61_conc = ec_top["M 61(ppbv)"]
M33_flux = ec_top["Flux_M 33"]
M45_flux = ec_top["Flux_M 45"]
M59_flux = ec_top["Flux_M 59"]
M69_flux = ec_top["Flux_M 69"]
M71_flux = ec_top["Flux_M 71"]
M81_flux = ec_top["Flux_M 81"]
M137_flux = ec_top["Flux_M 137"]
M87_flux = ec_top["Flux_M 87"]
M47_flux = ec_top["Flux_M 47"]
M61_flux = ec_top["Flux_M 61"]
我希望能够绘制这些气体浓度/通量随时间的演变，只有一个功能可以让我在绘制这些气体的浓度或通量之间进行选择。
这是我到目前为止所写的：
color_1 = 'black'
graph_type='conc'
fig, ((ax1, ax2, ax3), (ax5, ax7, ax8),(ax9,ax10,ax11)) = plt.subplots(3, 3, sharex=True, sharey=False)
fig.suptitle('Influence of wind direction of BVOCs concentration')
ax1.plot(wind_dir,'M33_'+graph_type,linestyle='',marker='.',color=color_1)
ax1.set_title('Methanol')
ax1.set(ylabel='Concentration [ppbv]')
ax2.plot(wind_dir,M39_conc,linestyle='',marker='.',color=color_1)
ax2.set_title('Water cluster')
ax2.set(ylabel='Concentration [ncps]')
ax3.plot(wind_dir,M45_conc,linestyle='',marker='.',color=color_1)
ax3.set_title('Acetaldehyde')
ax3.set(ylabel='Concentration [ppbv]')
# ax4.plot(wind_dir,M47_conc,linestyle='',marker='.',color='color_1')
# ax4.set_title('Unknown')
ax5.plot(wind_dir,M59_conc,linestyle='',marker='.',color=color_1)
ax5.set_title('Acetone')
ax5.set(ylabel='Concentration [ppbv]')
# ax6.plot(wind_dir,M61_conc,linestyle='',marker='.',color='color_1')
# ax6.set_title('Unknown')
ax7.plot(wind_dir,M69_conc,linestyle='',marker='.',color=color_1)
ax7.set_title('Isoprene')
ax7.set(ylabel='Concentration [ppbv]')
ax8.plot(wind_dir,M71_conc,linestyle='',marker='.',color=color_1)
ax8.set_title('Methyl vinyl, ketone and methacrolein')
ax8.set(ylabel='Concentration [ppbv]')
ax9.plot(wind_dir,M81_conc,linestyle='',marker='.',color=color_1)
ax9.set_title('Fragment of monoterpenes')
ax9.set(ylabel='Concentration [ppbv]',xlabel='Wind direction [°]')
ax10.plot(wind_dir,M87_conc,linestyle='',marker='.',color=color_1)
ax10.set_title('Methylbutenols')
ax10.set(ylabel='Concentration [ppbv]',xlabel='Wind direction [°]')
ax11.plot(wind_dir,M137_conc,linestyle='',marker='.',color=color_1)
ax11.set_title('Monoterpenes')
ax11.set(ylabel='Concentration [ppbv]',xlabel='Wind direction [°]')
plt.show()
当我尝试参数化要绘制的数据时，我会写，例如：
'M33_'+graph_type
我期望取值'M33_conc'。
有人可以帮我做到这一点吗？
提前致谢
解决方案
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_dirx 变量。在这个答案中，我忽略了这一点，而是使用时间作为 x 变量。
查看您的代码，我知道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熊猫的例子。
因为您想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（见df_subs下文）。我使用代码创建了一个提取单元并在此处共享它，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，该函数可以从数据框中选择要绘制的变量组ec_top。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是根据您的代码创建一些示例数据，以使您能够准确地重新创建我在此处显示的内容以及其他任何想尝试的人。所以如果你想使用这个解决方案，你可以跳过大部分，你需要做的就是按照你的方式创建物质表，然后调整绘图功能以适应你的喜好。
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
这些物质按照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，可让您使用参数选择绘图的graph_type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。variables变量按列表的顺序绘制。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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