python - Python 子图中的作用域类似于 MATLAB 的 stackedplot()

Question

Python 中是否有与 MATLAB 的 stackedplot() 相同的绘图函数？MATLAB 中的stackedplot() 可以绘制具有相同X 轴并垂直堆叠的多个变量的线图。此外，此图中有一个范围，只需移动光标即可显示给定 X 的所有变量的值(请参阅附图)。我已经能够在 Python 中毫无问题地生成堆叠的子图，但是，无法添加像这样的范围来通过移动光标显示所有变量的值。此功能在 Python 中可用吗？

这是使用 MATLAB 的 stackedplot() 绘制的图:

import pandas as pd
import numpy as np
from datetime import datetime, date, time
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.transforms as transforms
import mplcursors
from collections import Counter
import collections

def flatten(x):
    result = []
    for el in x:
        if isinstance(x, collections.Iterable) and not isinstance(el, str):
            result.extend(flatten(el))
        else:
            result.append(el)
    return result

def shared_scope(sel):
    sel.annotation.set_visible(False)  # hide the default annotation created by mplcursors
    x = sel.target[0]
    for ax in axes:
        for plot in plotStore:
            da = plot.get_ydata()
            if type(da[0]) is np.datetime64: #pd.Timestamp
                yData = matplotlib.dates.date2num(da) # to numerical values
                vals = np.interp(x, plot.get_xdata(), yData)
                dates = matplotlib.dates.num2date(vals) # to matplotlib dates
                y = datetime.strftime(dates,'%Y-%m-%d %H:%M:%S') # to strings
                annot = ax.annotate(f'{y:.30s}', (x, vals), xytext=(15, 10), textcoords='offset points',
                            bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
                sel.extras.append(annot)
            else:
                y = np.interp(x, plot.get_xdata(), plot.get_ydata())      
                annot = ax.annotate(f'{y:.2f}', (x, y), xytext=(15, 10), textcoords='offset points', arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"),
                            bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
                sel.extras.append(annot)
        vline = ax.axvline(x, color='k', ls=':')
        sel.extras.append(vline)
    trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
    text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
    sel.extras.append(text1)
        
   
# Data to plot
data = pd.DataFrame(columns = ['timeOfSample','Var1','Var2'])
data.timeOfSample = ['2020-05-10 09:09:02','2020-05-10 09:09:39','2020-05-10 09:40:07','2020-05-10 09:40:45','2020-05-12 09:50:45']
data['timeOfSample'] = pd.to_datetime(data['timeOfSample'])
data.Var1 = [10,50,100,5,25]
data.Var2 = [20,55,70,60,50]
variables = ['timeOfSample',['Var1','Var2']] # variables to plot - Var1 and Var2 to share a plot

nPlot = len(variables)   
dataPts = np.arange(0, len(data[variables[0]]), 1) # x values for plots
plotStore = [0]*len(flatten(variables)) # to store all the plots for annotation purposes later

fig, axes = plt.subplots(nPlot,1,sharex=True)

k=0
for i in range(nPlot):
    if np.size(variables[i])==1:
        yData = data[variables[i]]   
        line, = axes[i].plot(dataPts,yData,label = variables[i]) 
        plotStore[k]=line
        k = k+1
    else:
        for j in range(np.size(variables[i])): 
            yData = data[variables[i][j]]        
            line, = axes[i].plot(dataPts,yData,label = variables[i][j])             
            plotStore[k]=line
            k = k+1  
    axes[i].set_ylabel(variables[i])


cursor = mplcursors.cursor(plotStore, hover=True)
cursor.connect('add', shared_scope)
plt.xlabel('Samples')
plt.show()

Answer 1

mplcursors可用于在悬停、移动文本和垂直条时创建注释。 sel.extras.append(...) 有助于自动隐藏不再需要的元素。

import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import mplcursors
import numpy as np

def shared_scope(sel):
    x = sel.target[0]
    annotation_text = f'x: {x:.2f}'
    for ax, plot in zip(axes, all_plots):
        y = np.interp(x, plot.get_xdata(), plot.get_ydata())
        annotation_text += f'\n{plot.get_label()}: {y:.2f}'
        vline = ax.axvline(x, color='k', ls=':')
        sel.extras.append(vline)
    sel.annotation.set_text(annotation_text)
    trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
    text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
    sel.extras.append(text1)

fig, axes = plt.subplots(figsize=(15, 10), nrows=3, sharex=True)
y1 = np.random.uniform(-1, 1, 100).cumsum()
y2 = np.random.uniform(-1, 1, 100).cumsum()
y3 = np.random.uniform(-1, 1, 100).cumsum()
all_y = [y1, y2, y3]
all_labels = ['Var1', 'Var2', 'Var3']
all_plots = [ax.plot(y, label=label)[0]
             for ax, y, label in zip(axes, all_y, all_labels)]
for ax, label in zip(axes, all_labels):
    ax.set_ylabel(label)
cursor = mplcursors.cursor(all_plots, hover=True)
cursor.connect('add', shared_scope)

plt.show()

这是一个版本，每个子图都有单独的注释:

import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import mplcursors
import numpy as np

def shared_scope(sel):
    sel.annotation.set_visible(False)  # hide the default annotation created by mplcursors
    x = sel.target[0]
    for ax, plot in zip(axes, all_plots):
        y = np.interp(x, plot.get_xdata(), plot.get_ydata())
        vline = ax.axvline(x, color='k', ls=':')
        sel.extras.append(vline)
        annot = ax.annotate(f'{y:.2f}', (x, y), xytext=(5, 0), textcoords='offset points',
                            bbox=dict(facecolor='tomato', edgecolor='black', boxstyle='round', alpha=0.5))
        sel.extras.append(annot)
    trans = transforms.blended_transform_factory(axes[0].transData, axes[0].transAxes)
    text1 = axes[0].text(x, 1.01, f'{x:.2f}', ha='center', va='bottom', color='blue', clip_on=False, transform=trans)
    sel.extras.append(text1)

fig, axes = plt.subplots(figsize=(15, 10), nrows=3, sharex=True)
y1 = np.random.uniform(-1, 1, 100).cumsum()
y2 = np.random.uniform(-1, 1, 100).cumsum()
y3 = np.random.uniform(-1, 1, 100).cumsum()
all_y = [y1, y2, y3]
all_labels = ['Var1', 'Var2', 'Var3']
all_plots = [ax.plot(y, label=label)[0]
             for ax, y, label in zip(axes, all_y, all_labels)]
for ax, label in zip(axes, all_labels):
    ax.set_ylabel(label)
cursor = mplcursors.cursor(all_plots, hover=True)
cursor.connect('add', shared_scope)

plt.show()