Python结合matplotlib实现图表的基本交互
作者：MC皮蛋侠客
这篇文章主要为大家详细介绍了Python如何结合matplotlib实现图表的基本交互,可以实现图表的放大缩小和移动光标注释,感兴趣的小伙伴可以跟随小编一起学习一下
前言
最近在使用pyqt结合matplotlib开发一款内部使用的数据分析软件，发现matplotlib库在处理大数据，出图性能方面还是很不错的，但是就是图表的交互性上差了一点，比如说图像的放大和缩小，移动的光标线，显示注释等等，很多还是需要自己造轮子，本人通过五一假期的一番研究，从中也颇有收获，现在把下面的这些研究成果分享给大家。
使用Matplotlib库完成基本的图表交互
初始化基本的曲线配置
import sys
import numpy as np
from PySide6.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
class MatplotlibWidget(FigureCanvas):
def __init__(self, parent=None, width=5, height=4, dpi=100):
fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = fig.add_subplot(111)
self.compute_initial_figure()
FigureCanvas.__init__(self, fig)
self.setParent(parent)
self.lef_mouse_pressed = False # 鼠标左键是否按下
self.connect_event()
def connect_event(self):
return #添加鼠标事件，后续在这里添加
def compute_initial_figure(self):
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) * 2
self.line1, = self.axes.plot(x, y1, 'b-', label='sin(x)')
self.line2, = self.axes.plot(x, y2, 'r-', label='cos(x)')
self.line3, = self.axes.plot(x, y3, 'g-', label='2*sin(x)')
self.axes.legend() # 显示右上角标签
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.widget = QWidget()
self.setMinimumHeight(600)
self.setMinimumWidth(800)
self.showMaximized() # 设置全屏
self.setCentralWidget(self.widget)
layout = QVBoxLayout(self.widget)
self.mpl_widget = MatplotlibWidget(self.widget, width=5, height=4, dpi=100)
layout.addWidget(self.mpl_widget)
self.show()
if __name__ == "__main__":
app = QApplication(sys.argv)
mainWin = MainWindow()
sys.exit(app.exec_())
现在的图像应该是这个效果
添加鼠标滚动放大和缩小效果，比例可以自己设置，我这里设置的是最大值和最小值差值的10分之1
def on_mouse_wheel(self, event):
if self.axes is not None:
x_min, x_max = self.axes.get_xlim()
x_delta = (x_max - x_min) / 10 # 控制缩放X轴的比例
y_min, y_max = self.axes.get_ylim()
y_delta = (y_max - y_min) / 10 # 控制缩放X轴的比例
if event.button == "up":
self.axes.set(xlim=(x_min + x_delta, x_max - x_delta))
self.axes.set(ylim=(y_min + y_delta, y_max - y_delta))
elif event.button == "down":
self.axes.set(xlim=(x_min - x_delta, x_max + x_delta))
self.axes.set(ylim=(y_min - y_delta, y_max + y_delta))
self.draw_idle()
添加鼠标滚动事件到connect_event()函数里面
def connect_event(self):
self.mpl_connect("scroll_event", self.on_mouse_wheel) #鼠标滚动事件
实现按住鼠标向上下左右拖动的效果，拖动的距离同理可以自己控制
def on_button_press(self, event):
if event.inaxes is not None: # 判断是否在坐标轴内
if event.button == 1:
self.lef_mouse_pressed = True
self.pre_x = event.xdata
self.pre_y = event.ydata
def on_button_release(self, event):
self.lef_mouse_pressed = False
def on_mouse_move(self, event):
if event.inaxes is not None and event.button == 1:
if self.lef_mouse_pressed: #鼠标左键按下时才计算
x_delta = event.xdata - self.pre_x
y_delta = event.ydata - self.pre_y
# 获取当前原点和最大点的4个位置
x_min, x_max = self.axes.get_xlim()
y_min, y_max = self.axes.get_ylim()
# 控制一次移动鼠标拖动的距离
x_min = x_min - x_delta
x_max = x_max - x_delta
y_min = y_min - y_delta
y_max = y_max - y_delta
self.axes.set_xlim(x_min, x_max)
self.axes.set_ylim(y_min, y_max)
self.draw_idle()
添加鼠标按住和松开事件到connect_event()函数里面
def connect_event(self):
self.mpl_connect("scroll_event", self.on_mouse_wheel)
self.mpl_connect("button_press_event", self.on_button_press)
self.mpl_connect("button_release_event", self.on_button_release)
self.mpl_connect("motion_notify_event", self.on_mouse_move)
现在图像可以实现如下效果
可以看到鼠标按住后把图像拖动了，同时鼠标滚动也放大了图像
实现图表的高阶交互
我们最终想达到的目标是能够实现类似echarts库的功能，能够随着鼠标移动显示一条竖的光标线，光标线旁边能够显示详细信息，效果跟下图所示差不多
接下来我们使用matplotlib实现如图效果
给图表添加一个竖光标线，在图表中可以随着鼠标移动而移动，在光标线旁边显示相应的曲线信息
def init_annotation(self):
# 初始化光标线和注释
self.vertline, = self.ax.plot([], [], 'c-', lw=2)
# 预置一个空文本显示横坐标值
hPackerList = [HPacker(children=[TextArea("", textprops=dict(size=10))])]
for line in self.axes.get_lines():
if line == self.vertline: # 跳过光标线
continue
line_color = line.get_color() # 获取每条曲线的颜色
text_area = TextArea(line.get_label(), textprops=dict(size=10, color=line_color)) #根据曲线颜色设置文字颜色
hPacker = HPacker(children=[text_area])
hPackerList.append(hPacker)
self.text_box = VPacker(children=hPackerList, pad=1, sep=3) # 竖值布局，设置padding和文字之间上下的间距
self.annotation_bbox = AnnotationBbox(self.text_box, (0, 0),
xybox=(100, 0),
xycoords='data',
boxcoords="offset points")
if self.axes is not None:
self.axes.add_artist(self.annotation_bbox)
将init_annotation函数放到初始化init函数里面
添加hover函数，使竖光标线和注释随着鼠标的移动能够动态地做出改变，这里使用了annocation_bbox，这个工具网上资料很少，我也是临时翻英文文档看的，gpt生成的错误代码用不了，如果需要详细了解可以翻阅matlibplot的官方文档，hPacker和vPacker类似于qt的hBoxlayout和vBoxLayout，还是比较好理解的，还有textarea是可以设置颜色的
def hover(self, event):
if event.inaxes == self.axes:
x = event.xdata
if x is not None:
text = f"x: {x}" #显示横坐标值
hPacker_list = self.text_box.get_children()
time_hPacker = hPacker_list[0]
time_text_area: TextArea = time_hPacker.get_children()[0]
time_text_area.set_text(text) # 更新横坐标值
for index,line in enumerate(self.axes.get_lines()):
if line == self.vertline: # 跳过光标线
continue
x_data = line.get_xdata()
y_data = line.get_ydata()
y = np.interp(x, x_data, y_data)
# 更新光标线的位置
self.vertline.set_xdata([x, x])
self.vertline.set_ydata([self.axes.get_ylim()[0], self.axes.get_ylim()[1]])
# 显示每条曲线的详细信息
line_text = f"{line.get_label()}: {y:.3f}"
hPacker = hPacker_list[index+1] # 因为横坐标值放在了第一个hPacker中，所以从第二个开始
text_area: TextArea = hPacker.get_children()[0]
text_area.set_text(line_text)
# 更新AnnotationBbox的位置
self.annotation_bbox.xy = (x, event.ydata)
self.annotation_bbox.set_visible(True)
self.draw_idle()
else:
# 隐藏AnnotationBbox和光标线
self.annotation_bbox.set_visible(False)
self.vertline.set_xdata([])
self.vertline.set_ydata([])
self.draw_idle()
将hover函数添加到connect_event()函数中
def connect_event(self):
self.mpl_connect("scroll_event", self.on_mouse_wheel)
self.mpl_connect("button_press_event", self.on_button_press)
self.mpl_connect("button_release_event", self.on_button_release)
self.mpl_connect("motion_notify_event", self.on_mouse_move)
self.mpl_connect("motion_notify_event", self.hover)
最终实现效果
全部代码
import sys
import numpy as np
from PySide6.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.offsetbox import HPacker, TextArea, VPacker, AnnotationBbox
class MatplotlibWidget(FigureCanvas):
def __init__(self, parent=None, width=5, height=4, dpi=100):
fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = fig.add_subplot(111)
self.lef_mouse_pressed = False # 鼠标左键是否按下
self.compute_initial_figure()
FigureCanvas.__init__(self, fig)
self.setParent(parent)
self.connect_event()
self.init_annotation()
def connect_event(self):
self.mpl_connect("scroll_event", self.on_mouse_wheel)
self.mpl_connect("button_press_event", self.on_button_press)
self.mpl_connect("button_release_event", self.on_button_release)
self.mpl_connect("motion_notify_event", self.on_mouse_move)
self.mpl_connect("motion_notify_event", self.hover)
def compute_initial_figure(self):
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) * 2
self.line1, = self.axes.plot(x, y1, 'b-', label='sin(x)')
self.line2, = self.axes.plot(x, y2, 'r-', label='cos(x)')
self.line3, = self.axes.plot(x, y3, 'g-', label='2*sin(x)')
self.axes.legend() # 显示右上角标签
def init_annotation(self):
# 初始化光标线和注释
self.vertline, = self.axes.plot([], [], 'c-', lw=2)
hPackerList = [HPacker(children=[TextArea("", textprops=dict(size=10))])] # 预置一个空文本显示横坐标值
for line in self.axes.get_lines():
if line == self.vertline: # 跳过光标线
continue
line_color = line.get_color() # 获取每条曲线的颜色
text_area = TextArea(line.get_label(), textprops=dict(size=10, color=line_color)) #根据曲线颜色设置文字颜色
hPacker = HPacker(children=[text_area])
hPackerList.append(hPacker)
self.text_box = VPacker(children=hPackerList, pad=1, sep=3) # 竖值布局，设置padding和文字之间上下的间距
self.annotation_bbox = AnnotationBbox(self.text_box, (0, 0),
xybox=(100, 0),
xycoords='data',
boxcoords="offset points")
if self.axes is not None:
self.axes.add_artist(self.annotation_bbox)
def on_mouse_wheel(self, event):
if self.axes is not None:
x_min, x_max = self.axes.get_xlim()
x_delta = (x_max - x_min) / 10
y_min, y_max = self.axes.get_ylim()
y_delta = (y_max - y_min) / 10
if event.button == "up":
self.axes.set(xlim=(x_min + x_delta, x_max - x_delta))
self.axes.set(ylim=(y_min + y_delta, y_max - y_delta))
elif event.button == "down":
self.axes.set(xlim=(x_min - x_delta, x_max + x_delta))
self.axes.set(ylim=(y_min - y_delta, y_max + y_delta))
self.draw_idle()
def on_button_press(self, event):
if event.inaxes is not None: # 判断是否在坐标轴内
if event.button == 1:
self.lef_mouse_pressed = True
self.pre_x = event.xdata
self.pre_y = event.ydata
def on_button_release(self, event):
self.lef_mouse_pressed = False
def on_mouse_move(self, event):
if event.inaxes is not None and event.button == 1:
if self.lef_mouse_pressed:
x_delta = event.xdata - self.pre_x
y_delta = event.ydata - self.pre_y
# 获取当前原点和最大点的4个位置
x_min, x_max = self.axes.get_xlim()
y_min, y_max = self.axes.get_ylim()
x_min = x_min - x_delta
x_max = x_max - x_delta
y_min = y_min - y_delta
y_max = y_max - y_delta
self.axes.set_xlim(x_min, x_max)
self.axes.set_ylim(y_min, y_max)
self.draw_idle()
def hover(self, event):
if event.inaxes == self.axes:
x = event.xdata
if x is not None:
text = f"x: {x}" #显示横坐标值
hPacker_list = self.text_box.get_children()
time_hPacker = hPacker_list[0]
time_text_area: TextArea = time_hPacker.get_children()[0]
time_text_area.set_text(text) # 更新横坐标值
for index,line in enumerate(self.axes.get_lines()):
if line == self.vertline: # 跳过光标线
continue
x_data = line.get_xdata()
y_data = line.get_ydata()
y = np.interp(x, x_data, y_data)
# 更新光标线的位置
self.vertline.set_xdata([x, x])
self.vertline.set_ydata([self.axes.get_ylim()[0], self.axes.get_ylim()[1]])
# 显示每条曲线的详细信息
line_text = f"{line.get_label()}: {y:.3f}"
hPacker = hPacker_list[index+1] # 因为横坐标值放在了第一个hPacker中，所以从第二个开始
text_area: TextArea = hPacker.get_children()[0]
text_area.set_text(line_text)
# 更新AnnotationBbox的位置
self.annotation_bbox.xy = (x, event.ydata)
self.annotation_bbox.set_visible(True)
self.draw_idle()
else:
# 隐藏AnnotationBbox和光标线
self.annotation_bbox.set_visible(False)
self.vertline.set_xdata([])
self.vertline.set_ydata([])
self.draw_idle()
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.widget = QWidget()
self.setMinimumHeight(600)
self.setMinimumWidth(800)
self.showMaximized() # 设置全屏
self.setCentralWidget(self.widget)
layout = QVBoxLayout(self.widget)
self.mpl_widget = MatplotlibWidget(self.widget, width=5, height=4, dpi=100)
layout.addWidget(self.mpl_widget)
self.show()
if __name__ == "__main__":
app = QApplication(sys.argv)
mainWin = MainWindow()
sys.exit(app.exec_())
总结
感觉matplotlib库更适用于静态图表的分析，即使自己造了这些轮子，但总感觉还是不如echats好用，不过echarts性能方面还是不如matplotlib的，毕竟两者的应用场景确实不一样。
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