5. 色带集合 API¶

ColormapCollection 提供链式筛选、分组、HTML 导出和批量出图。

链式筛选¶

相比 cmap.list() 的一次性传参，链式 .filter() 更灵活、可读性更好。

In [1]:

from multicolor import cmap

# 分步筛选：先选内置，再选 sequential，再选色盲安全
result = (
    cmap.collection(is_custom=False)
    .filter(cmap_type="sequential")
    .filter(colorblind_safe="any")
)
print(f"Found {len(result)} colormaps: {result.names}")

from multicolor import cmap # 分步筛选：先选内置，再选 sequential，再选色盲安全 result = ( cmap.collection(is_custom=False) .filter(cmap_type="sequential") .filter(colorblind_safe="any") ) print(f"Found {len(result)} colormaps: {result.names}")

Found 42 colormaps: ['Autumn', 'Berlin', 'Binary', 'Blues', 'Bone', 'BuGn', 'BuPu', 'Cividis', 'Cool', 'Copper', 'Cubehelix', 'Gist_earth', 'Gist_gray', 'Gist_yarg', 'GnBu', 'Gray', 'Greens', 'Greys', 'Inferno', 'Magma', 'Managua', 'Ocean', 'OrRd', 'Oranges', 'Plasma', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'Spring', 'Summer', 'Turbo', 'Vanimo', 'Viridis', 'Winter', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']

In [2]:

# 在筛选结果上继续筛选
result = result.filter(source="matplotlib")
print(f"After filtering by source: {result.names}")

# 在筛选结果上继续筛选 result = result.filter(source="matplotlib") print(f"After filtering by source: {result.names}")

After filtering by source: ['Autumn', 'Berlin', 'Binary', 'Blues', 'Bone', 'BuGn', 'BuPu', 'Cividis', 'Cool', 'Copper', 'Cubehelix', 'Gist_earth', 'Gist_gray', 'Gist_yarg', 'GnBu', 'Gray', 'Greens', 'Greys', 'Inferno', 'Magma', 'Managua', 'Ocean', 'OrRd', 'Oranges', 'Plasma', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'Spring', 'Summer', 'Turbo', 'Vanimo', 'Viridis', 'Winter', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']

分组¶

group_by() 按字段分组，返回 {key: ColormapCollection}。

In [3]:

# 按类型分组
all_cmaps = cmap.collection(is_custom=False)
groups = all_cmaps.group_by("cmap_type")
for key, group in groups.items():
    print(f"{key} ({len(group)}): {group.names}")

# 按类型分组 all_cmaps = cmap.collection(is_custom=False) groups = all_cmaps.group_by("cmap_type") for key, group in groups.items(): print(f"{key} ({len(group)}): {group.names}")

qualitative (12): ['Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'Tab10', 'Tab20', 'Tab20b', 'Tab20c']
sequential (56): ['Afmhot', 'Autumn', 'Berlin', 'Binary', 'Blues', 'Bone', 'BuGn', 'BuPu', 'CMRmap', 'Cividis', 'Cool', 'Copper', 'Cubehelix', 'Gist_earth', 'Gist_gray', 'Gist_heat', 'Gist_ncar', 'Gist_stern', 'Gist_yarg', 'GnBu', 'Gnuplot', 'Gnuplot2', 'Gray', 'Greens', 'Greys', 'Hot', 'Hsv', 'Inferno', 'Jet', 'Magma', 'Managua', 'Nipy_spectral', 'Ocean', 'OrRd', 'Oranges', 'Pink', 'Plasma', 'Prism', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'Rainbow', 'RdPu', 'Reds', 'Spring', 'Summer', 'Turbo', 'Vanimo', 'Viridis', 'Winter', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']
diverging (17): ['BrBG', 'Bwr', 'Coolwarm', 'Flag', 'Gist_rainbow', 'PRGn', 'PiYG', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Seismic', 'Spectral', 'Terrain', 'Twilight', 'Twilight_shifted']

In [4]:

# 按来源分组
source_groups = cmap.collection().group_by("source")
for key, group in source_groups.items():
    print(f"{key}: {group.names}")

# 按来源分组 source_groups = cmap.collection().group_by("source") for key, group in source_groups.items(): print(f"{key}: {group.names}")

matplotlib: ['Accent', 'Afmhot', 'Autumn', 'Berlin', 'Binary', 'Blues', 'Bone', 'BrBG', 'BuGn', 'BuPu', 'Bwr', 'CMRmap', 'Cividis', 'Cool', 'Coolwarm', 'Copper', 'Cubehelix', 'Dark2', 'Flag', 'Gist_earth', 'Gist_gray', 'Gist_heat', 'Gist_ncar', 'Gist_rainbow', 'Gist_stern', 'Gist_yarg', 'GnBu', 'Gnuplot', 'Gnuplot2', 'Gray', 'Greens', 'Greys', 'Hot', 'Hsv', 'Inferno', 'Jet', 'Magma', 'Managua', 'Nipy_spectral', 'Ocean', 'OrRd', 'Oranges', 'PRGn', 'Paired', 'Pastel1', 'Pastel2', 'PiYG', 'Pink', 'Plasma', 'Prism', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'Rainbow', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Seismic', 'Set1', 'Set2', 'Set3', 'Spectral', 'Spring', 'Summer', 'Tab10', 'Tab20', 'Tab20b', 'Tab20c', 'Terrain', 'Turbo', 'Twilight', 'Twilight_shifted', 'Vanimo', 'Viridis', 'Winter', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']

HTML 报告导出¶

to_html() 生成包含色带可视化预览的 HTML 页面。

In [5]:

import os
from IPython.display import IFrame

collection = cmap.collection(is_custom=False).filter(colorblind_safe="any")
print(f"Exporting {len(collection)} colormaps to HTML...")

# 保存到 notebook 同目录下，执行后可直接打开
path = os.path.join(os.getcwd(), "colorblind_report.html")
collection.to_html(path, cols=3, title="Colorblind-Safe Colormaps")

print(f"Saved to: {path}")

# 在 Jupyter 中直接内嵌预览
IFrame(path, width=800, height=600)

import os from IPython.display import IFrame collection = cmap.collection(is_custom=False).filter(colorblind_safe="any") print(f"Exporting {len(collection)} colormaps to HTML...") # 保存到 notebook 同目录下，执行后可直接打开 path = os.path.join(os.getcwd(), "colorblind_report.html") collection.to_html(path, cols=3, title="Colorblind-Safe Colormaps") print(f"Saved to: {path}") # 在 Jupyter 中直接内嵌预览 IFrame(path, width=800, height=600)

Exporting 53 colormaps to HTML...
Saved to: /home/runner/work/multicolor/multicolor/docs/examples/colorblind_report.html

Out[5]: