使用Python制作动态图表的实战指南

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np

# 初始化数据
np.random.seed(0)
data = np.random.rand(10)
labels = [f'Item {i}' for i in range(10)]

# 创建图形和轴
fig, ax = plt.subplots()
bars = ax.barh(labels, data, color='skyblue')

# 更新函数，用于动画的每一帧
def update(frame):
    new_data = np.random.rand(10)
    for bar, new_val in zip(bars, new_data):
        bar.set_width(new_val)
    return bars

# 创建动画
ani = FuncAnimation(fig, update, frames=10, interval=1000, blit=True)

plt.show()

import plotly.graph_objects as go
import numpy as np

# 生成数据
x = np.linspace(0, 10, 100)
y_values = [np.sin(x + i/5.0) for i in range(10)]

# 创建动态折线图
fig = go.Figure()

for i, y in enumerate(y_values):
    fig.add_trace(go.Scatter(x=x, y=y, name=f'Line {i+1}', mode='lines'))

# 添加滑块控件
steps = []
for i in range(10):
    step = dict(
        method="update",
        args=[{"visible": [j == i for j in range(10)]}],
        label=f"Line {i+1}"
    )
    steps.append(step)

sliders = [dict(
    active=0,
    currentvalue={"prefix": "Visible: "},
    pad={"t": 50},
    steps=steps
)]

fig.update_layout(sliders=sliders)
fig.show()

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, Slider
from bokeh.layouts import column
from bokeh.io import curdoc
import numpy as np

# 生成数据
x = np.linspace(0, 10, 100)
y_base = np.sin(x)

# 创建ColumnDataSource
source = ColumnDataSource(data=dict(x=x, y=y_base))

# 创建图形
p = figure(title="Dynamic Scatter Plot", x_range=(0, 10), y_range=(-1.5, 1.5))
p.scatter('x', 'y', source=source, size=10, color="navy", alpha=0.5)

# 创建滑块控件
def update(attr, old, new):
    phase = slider.value
    new_y = np.sin(x + phase)
    source.data = dict(x=x, y=new_y)

slider = Slider(title="Phase", value=0.0, start=0.0, end=10.0, step=0.1)
slider.on_change('value', update)

# 布局和显示
layout = column(slider, p)
curdoc().add_root(layout)
show(layout)