7. 装饰器 + matplotlib 多图表¶

演示如何用装饰器让同一绘图函数自动用不同色带渲染多种图表。

方式一：single 模式 — 多组柱状图¶

每个色带独立弹窗展示柱状图。

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from multicolor import cmap, batch_cmaps

# 模拟数据
categories = ["Q1", "Q2", "Q3", "Q4"]
values = [23, 45, 56, 78]
x = np.arange(len(categories))


@batch_cmaps(["Viridis", "Plasma", "Inferno"])
def show_bar_chart(cmap_obj, ax=None):
    colors = cmap_obj(np.linspace(0, 1, len(values)))
    if ax is None:
        fig, ax = plt.subplots(figsize=(6, 4))
    ax.bar(categories, values, color=colors)
    ax.set_title(f"Seasonal Sales ({cmap_obj.name})")
    ax.set_ylabel("Revenue")


show_bar_chart()

import numpy as np import matplotlib.pyplot as plt from multicolor import cmap, batch_cmaps # 模拟数据 categories = ["Q1", "Q2", "Q3", "Q4"] values = [23, 45, 56, 78] x = np.arange(len(categories)) @batch_cmaps(["Viridis", "Plasma", "Inferno"]) def show_bar_chart(cmap_obj, ax=None): colors = cmap_obj(np.linspace(0, 1, len(values))) if ax is None: fig, ax = plt.subplots(figsize=(6, 4)) ax.bar(categories, values, color=colors) ax.set_title(f"Seasonal Sales ({cmap_obj.name})") ax.set_ylabel("Revenue") show_bar_chart()

方式二：single 模式 — 多组折线图¶

In [2]:

import numpy as np
import matplotlib.pyplot as plt
from multicolor import cmap, batch_cmaps

np.random.seed(42)
x = np.linspace(0, 10, 50)


@batch_cmaps(["Cividis", "Coolwarm", "RdYlGn"])
def show_line_chart(cmap_obj, ax=None):
    if ax is None:
        fig, ax = plt.subplots(figsize=(6, 4))
    # 每条线用色带中一个颜色
    colors = cmap_obj(np.linspace(0, 1, 5))
    for i in range(5):
        y = np.sin(x + i * 0.5) + np.random.randn(50) * 0.1
        ax.plot(x, y, color=colors[i], linewidth=2, label=f"Line {i+1}")
    ax.set_title(f"Multi-line Plot ({cmap_obj.name})")
    ax.set_xlabel("Time")
    ax.set_ylabel("Value")
    ax.legend(fontsize="small", loc="upper right")


show_line_chart()

import numpy as np import matplotlib.pyplot as plt from multicolor import cmap, batch_cmaps np.random.seed(42) x = np.linspace(0, 10, 50) @batch_cmaps(["Cividis", "Coolwarm", "RdYlGn"]) def show_line_chart(cmap_obj, ax=None): if ax is None: fig, ax = plt.subplots(figsize=(6, 4)) # 每条线用色带中一个颜色 colors = cmap_obj(np.linspace(0, 1, 5)) for i in range(5): y = np.sin(x + i * 0.5) + np.random.randn(50) * 0.1 ax.plot(x, y, color=colors[i], linewidth=2, label=f"Line {i+1}") ax.set_title(f"Multi-line Plot ({cmap_obj.name})") ax.set_xlabel("Time") ax.set_ylabel("Value") ax.legend(fontsize="small", loc="upper right") show_line_chart()

方式三：grid 模式 — 所有折线图在一张图上¶

In [3]:

import numpy as np
import matplotlib.pyplot as plt
from multicolor import cmap, batch_cmaps

np.random.seed(42)
x = np.linspace(0, 10, 50)


@batch_cmaps(
    query_func=lambda: cmap.names(cmap_type="diverging", is_custom=False),
    layout="grid",
    cols=2,
)
def show_diverging_lines(cmap_obj, ax=None):
    colors = cmap_obj(np.linspace(0, 1, 5))
    for i in range(5):
        y = np.sin(x + i * 0.8) + np.random.randn(50) * 0.1
        ax.plot(x, y, color=colors[i], linewidth=2)
    ax.set_title(cmap_obj.name, fontsize=10)


show_diverging_lines()

import numpy as np import matplotlib.pyplot as plt from multicolor import cmap, batch_cmaps np.random.seed(42) x = np.linspace(0, 10, 50) @batch_cmaps( query_func=lambda: cmap.names(cmap_type="diverging", is_custom=False), layout="grid", cols=2, ) def show_diverging_lines(cmap_obj, ax=None): colors = cmap_obj(np.linspace(0, 1, 5)) for i in range(5): y = np.sin(x + i * 0.8) + np.random.randn(50) * 0.1 ax.plot(x, y, color=colors[i], linewidth=2) ax.set_title(cmap_obj.name, fontsize=10) show_diverging_lines()

方式四：grid 模式 — 分组柱状图对比¶

比较不同类型色带在同一数据上的视觉效果。

In [4]:

import numpy as np
import matplotlib.pyplot as plt
from multicolor import cmap, batch_cmaps

np.random.seed(42)
groups = ["A", "B", "C", "D", "E"]
values1 = [20, 35, 30, 35, 27]
values2 = [25, 32, 34, 20, 25]
x = np.arange(len(groups))
width = 0.35


@batch_cmaps(["Viridis", "RdYlGn", "Terrain"])
def show_grouped_bars(cmap_obj, ax=None):
    if ax is None:
        fig, ax = plt.subplots(figsize=(8, 5))
    # 从色带中取 5 个均匀分布的颜色
    cmap_colors = cmap_obj(np.linspace(0, 1, 5))
    ax.bar(x - width / 2, values1, width, color=cmap_colors, label="Group 1")
    ax.bar(x + width / 2, values2, width, color=cmap_colors[::-1], label="Group 2")
    ax.set_title(f"Comparison ({cmap_obj.name})")
    ax.set_xticks(x)
    ax.set_xticklabels(groups)
    ax.legend(fontsize="small")


show_grouped_bars()

import numpy as np import matplotlib.pyplot as plt from multicolor import cmap, batch_cmaps np.random.seed(42) groups = ["A", "B", "C", "D", "E"] values1 = [20, 35, 30, 35, 27] values2 = [25, 32, 34, 20, 25] x = np.arange(len(groups)) width = 0.35 @batch_cmaps(["Viridis", "RdYlGn", "Terrain"]) def show_grouped_bars(cmap_obj, ax=None): if ax is None: fig, ax = plt.subplots(figsize=(8, 5)) # 从色带中取 5 个均匀分布的颜色 cmap_colors = cmap_obj(np.linspace(0, 1, 5)) ax.bar(x - width / 2, values1, width, color=cmap_colors, label="Group 1") ax.bar(x + width / 2, values2, width, color=cmap_colors[::-1], label="Group 2") ax.set_title(f"Comparison ({cmap_obj.name})") ax.set_xticks(x) ax.set_xticklabels(groups) ax.legend(fontsize="small") show_grouped_bars()

方式五：ColormapRenderer 批量导出¶

不用弹窗，直接导出为文件。

In [5]:

import os
import numpy as np
import matplotlib.pyplot as plt
from multicolor.render import ColormapRenderer
from multicolor import cmap

# 获取色带
items = cmap.list(cmap_type="sequential", tag="common", is_custom=False)
cmaps = [(name, cm) for _, name, cm in items]

# 用 render_grid 预览
fig, axes = ColormapRenderer.render_grid(cmaps, cols=3, size_per_colormap=(3, 1.5))
plt.show()

# 批量导出为 PNG
outdir = os.path.join(os.getcwd(), "cmap_comparisons")
col = cmap.collection(cmap_type="sequential", tag="common", is_custom=False)
col.export_images(outdir, orientation="horizontal", size=(4, 1), dpi=150)
print(f"Exported to {outdir}:")
for f in sorted(os.listdir(outdir)):
    print(f"  {f}")
plt.close("all")

import os import numpy as np import matplotlib.pyplot as plt from multicolor.render import ColormapRenderer from multicolor import cmap # 获取色带 items = cmap.list(cmap_type="sequential", tag="common", is_custom=False) cmaps = [(name, cm) for _, name, cm in items] # 用 render_grid 预览 fig, axes = ColormapRenderer.render_grid(cmaps, cols=3, size_per_colormap=(3, 1.5)) plt.show() # 批量导出为 PNG outdir = os.path.join(os.getcwd(), "cmap_comparisons") col = cmap.collection(cmap_type="sequential", tag="common", is_custom=False) col.export_images(outdir, orientation="horizontal", size=(4, 1), dpi=150) print(f"Exported to {outdir}:") for f in sorted(os.listdir(outdir)): print(f" {f}") plt.close("all")

Exported to /home/runner/work/multicolor/multicolor/docs/examples/cmap_comparisons:
  Cividis.png
  Inferno.png
  Jet.png
  Magma.png
  Plasma.png
  Turbo.png
  Viridis.png