a65adcc2e5
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:
- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
- docx family: kept docx (official, full toolchain) + docx-cn
(GB/T 9704 Chinese official-document constants),
dropped docx_writer (no scripts, name collided with docx)
- humanizer family: kept humanizer-zh (6 zh reference docs),
dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
live credentials
Result: 25 skills, 572 files, ~7.5 MB.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
365 lines
12 KiB
Python
365 lines
12 KiB
Python
"""
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公共 API - 简洁的高层接口
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这是用户（您的工作室同事）唯一需要导入的模块。
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隐藏所有实现细节。
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"""
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from typing import List, Dict, Optional
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from pptx.slide import Slide
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from pptx.enum.chart import XL_CHART_TYPE
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from pptx.chart.data import CategoryChartData
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from pptx.util import Inches
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import pandas as pd
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from .builder import ChartBuilder
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# 导入样式模块
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try:
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from .styles import StyleConfig, DEFAULT_STYLE_CONFIG
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except ImportError:
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StyleConfig = None
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DEFAULT_STYLE_CONFIG = None
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# 导入布局模块
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try:
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from .layout import ChartLayoutConfig
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except ImportError:
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ChartLayoutConfig = None
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def create_combo_chart(
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slide: Slide,
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df: pd.DataFrame,
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categories_col: str,
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series_config: List[Dict],
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position: tuple = (Inches(1), Inches(2)),
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size: tuple = (Inches(8), Inches(4.5)),
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style_config=None,
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layout_config=None,
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):
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"""
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创建组合图（支持 P(n,2) 任意组合）
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Args:
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slide: 幻灯片对象
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df: 数据 DataFrame
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categories_col: 分类列名（X 轴）
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series_config: 系列配置列表
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[
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{"key": "销售额", "name": "销售额", "type": "bar", "axis": "primary"},
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{"key": "增长率", "name": "增长率", "type": "line", "axis": "secondary"},
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{"key": "市场份额", "name": "市场份额", "type": "line", "axis": "secondary"},
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]
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position: 图表位置 (left, top)
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size: 图表大小 (width, height)
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style_config: 样式配置对象（可选，默认使用 DEFAULT_STYLE_CONFIG）
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可以是 StyleConfig 实例，或 None 使用默认样式
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layout_config: 布局配置对象（可选）
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可以是 ChartLayoutConfig 实例，包含图例、轴配置
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Returns:
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Chart 对象
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Examples:
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>>> # 示例 1: 使用默认样式和布局
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>>> chart = create_combo_chart(
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... slide=slide,
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... df=df,
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... categories_col="日期",
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... series_config=[
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... {"key": "销售额", "name": "销售额", "type": "bar", "axis": "primary"},
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... {"key": "增长率", "name": "增长率", "type": "line", "axis": "secondary"},
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... ]
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... )
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>>> # 示例 2: 自定义样式 + 布局
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>>> from ppt_station.chart_builder.styles import StyleConfig
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>>> from ppt_station.chart_builder.layout import (
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... ChartLayoutConfig,
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... LegendConfig,
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... CategoryAxisConfig,
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... )
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>>>
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>>> # 样式配置
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>>> custom_style = StyleConfig(
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... color_scheme="dark_only",
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... line_width_pt=1.5,
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... marker_style="none",
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... )
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>>>
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>>> # 布局配置
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>>> custom_layout = ChartLayoutConfig(
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... legend_config=LegendConfig(position="bottom", font_size_pt=10),
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... category_axis_config=CategoryAxisConfig(
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... is_date_axis=True,
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... major_unit_days=7, # 每周显示一个刻度
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... number_format="yyyy-mm-dd",
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... ),
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... )
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>>>
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>>> chart = create_combo_chart(
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... slide=slide,
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... df=df,
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... categories_col="日期",
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... series_config=[...],
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... style_config=custom_style,
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... layout_config=custom_layout
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... )
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Supported Combinations:
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- type: 'bar', 'column', 'line', 'area' (散点图待实现)
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- axis: 'primary', 'secondary'
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- 任意 (type1, axis1) + (type2, axis2) 的组合
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- 支持主轴多种类型，次轴多种类型
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Notes:
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- 分类（X轴）在 Excel 中占用 A 列
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- 系列数据从 B 列开始
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- 支持超过 25 个系列（AA, AB, ...）
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- 左轴标签在左侧，右轴标签在右侧，不会重叠
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- 默认样式：无标记点、1pt 线宽、深浅色交替
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- 默认布局：图例在底部、横轴普通分类轴
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"""
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if not series_config:
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raise ValueError("series_config 不能为空")
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# 1. 按 (type, axis) 分组（用于决定引导图表类型）
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plot_groups = _group_series(series_config)
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# 2. 创建引导图表（写入全部系列数据到嵌入 Excel）
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chart = _bootstrap_chart(
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slide, df, categories_col, series_config, position, size
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)
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# ⭐ 核心修复：修正嵌入的 Excel 工作表中的日期数据
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# 如果分类列是日期类型，需要将 Excel 工作表中的文本日期转换为真实的日期数值
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_fix_embedded_excel_dates(chart, df, categories_col)
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# 3. 使用构建器完成剩余工作（传递样式配置和布局配置）
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builder = ChartBuilder(
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chart,
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df,
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categories_col,
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style_config=style_config if style_config is not None else DEFAULT_STYLE_CONFIG,
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layout_config=layout_config
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)
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# 注意：引导图表已经创建了第一个系列，构建器会继续追加
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# 如果需要完全自定义，可以在 builder.clear_bootstrap_chart() 中清理
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return builder.build(series_config)
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def _bootstrap_chart(
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slide: Slide,
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df: pd.DataFrame,
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categories_col: str,
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series_config: List[Dict],
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position: tuple,
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size: tuple,
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):
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"""
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创建引导图表（写入全部系列数据到嵌入 Excel）
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用途：
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- 激活 <c:plotArea>，使其可以通过 XML 访问
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- 创建初始的分类轴和值轴
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- 将所有系列数据写入嵌入 Excel（确保"编辑数据"不丢数据）
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- 图表 XML 结构后续由 ChartBuilder 重建
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Args:
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slide: 幻灯片对象
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df: 数据 DataFrame
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categories_col: 分类列名
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series_config: 全部系列配置列表
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position: (left, top)
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size: (width, height)
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Returns:
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Chart 对象
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"""
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chart_data = CategoryChartData()
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# 设置分类（X轴）
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categories = df[categories_col].tolist()
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if pd.api.types.is_datetime64_any_dtype(df[categories_col]):
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categories_bootstrap = [cat.strftime("%Y-%m-%d") if hasattr(cat, 'strftime') else str(cat) for cat in categories]
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else:
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categories_bootstrap = categories
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chart_data.categories = categories_bootstrap
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# 添加全部系列数据（确保嵌入 Excel 包含所有列）
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for series_cfg in series_config:
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chart_data.add_series(
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series_cfg["name"],
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df[series_cfg["key"]].tolist()
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)
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# 使用第一个系列的类型决定引导图表类型
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chart_type = _get_chart_type(series_config[0].get("type", "bar"))
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# 创建图表
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left, top = position
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width, height = size
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graphic_frame = slide.shapes.add_chart(
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chart_type, left, top, width, height, chart_data
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)
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return graphic_frame.chart
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def _get_chart_type(type_str: str) -> XL_CHART_TYPE:
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"""将图表类型字符串转换为 XL_CHART_TYPE 枚举"""
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type_map = {
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"bar": XL_CHART_TYPE.COLUMN_CLUSTERED,
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"column": XL_CHART_TYPE.COLUMN_CLUSTERED,
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"line": XL_CHART_TYPE.LINE,
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"area": XL_CHART_TYPE.AREA,
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}
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return type_map.get(type_str.lower(), XL_CHART_TYPE.COLUMN_CLUSTERED)
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def _group_series(series_config: List[Dict]) -> Dict[tuple, List[Dict]]:
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"""按 (type, axis) 分组系列"""
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from collections import defaultdict
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groups = defaultdict(list)
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for cfg in series_config:
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key = (cfg.get("type", "bar"), cfg.get("axis", "primary"))
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groups[key].append(cfg)
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return dict(groups)
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def _fix_embedded_excel_dates(chart, df: pd.DataFrame, categories_col: str):
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"""
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修正嵌入的 Excel 工作表中的日期数据
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新方案：将日期格式化为字符串标签（如 "2024/01"）
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这样 PowerPoint 就会正确显示，而不会出现 1900 年问题
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Args:
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chart: python-pptx Chart 对象
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df: 数据 DataFrame
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categories_col: 分类列名
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"""
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# 检查是否为日期类型
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if not pd.api.types.is_datetime64_any_dtype(df[categories_col]):
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print(f" → 分类列不是日期类型，跳过 Excel 工作表修正")
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return # 不是日期类型，无需修正
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print(f"\n🔧 修正嵌入的 Excel 工作表日期数据（转换为格式化字符串）...")
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try:
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from datetime import datetime
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from openpyxl import load_workbook
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import io
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# 获取嵌入的 Excel 数据
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chart_part = chart.part
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xlsx_part = chart_part.chart_workbook.xlsx_part
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print(f" → 找到嵌入的 Excel 工作表")
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# 将 Excel blob 加载为 openpyxl workbook
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xlsx_stream = io.BytesIO(xlsx_part.blob)
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wb = load_workbook(xlsx_stream)
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ws = wb.active
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print(f" → 工作表行数: {ws.max_row}, 列数: {ws.max_column}")
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# 获取日期数据
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categories = df[categories_col].tolist()
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print(f" → 准备修正 {len(categories)} 个日期值")
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print(f" → 第一个值: {categories[0]} (类型: {type(categories[0])})")
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# 修正 A 列（分类列）的数据 - 转换为格式化字符串
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# Excel 工作表的第一行是表头，数据从第二行开始
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fixed_count = 0
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for i, cat_value in enumerate(categories, start=2):
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if hasattr(cat_value, 'to_pydatetime'):
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cat_value = cat_value.to_pydatetime()
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if isinstance(cat_value, datetime):
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# ⭐ 将日期格式化为字符串 "yyyy/mm"（年份/月份）
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date_str = cat_value.strftime('%Y/%m')
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ws.cell(row=i, column=1).value = date_str
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# 不设置数字格式，保持为文本
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fixed_count += 1
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print(f" → 已修正 {fixed_count} 个单元格")
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print(f" → 示例：{categories[0].strftime('%Y/%m') if isinstance(categories[0], datetime) or hasattr(categories[0], 'strftime') else 'N/A'}")
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# 将修改后的 workbook 写回 blob
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output_stream = io.BytesIO()
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wb.save(output_stream)
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xlsx_part._blob = output_stream.getvalue()
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print(f" ✅ 嵌入 Excel 工作表修正完成（{fixed_count} 个日期值转换为格式化字符串）")
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except Exception as e:
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print(f" ⚠️ 修正嵌入 Excel 工作表失败: {e}")
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import traceback
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traceback.print_exc()
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# ============================================================================
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# 便捷函数：向后兼容
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# ============================================================================
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def create_dual_axis_chart(
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slide: Slide,
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df: pd.DataFrame,
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categories_col: str,
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bar_columns: List[str],
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bar_names: List[str],
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line_columns: List[str],
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line_names: List[str],
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position: tuple = (Inches(1), Inches(2)),
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size: tuple = (Inches(8), Inches(4.5)),
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):
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"""
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便捷函数：创建双轴组合图（柱状图 + 折线图）
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这是向后兼容的 API，与旧的 xml_chart_patcher 接口一致。
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Example:
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>>> create_dual_axis_chart(
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... slide=slide,
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... df=df,
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... categories_col="日期",
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... bar_columns=["销售额", "成本"],
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... bar_names=["销售额", "成本"],
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... line_columns=["利润率"],
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... line_names=["利润率"],
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... )
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"""
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# 构建统一的 series_config
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series_config = []
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# 主轴柱状图
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for col, name in zip(bar_columns, bar_names):
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series_config.append({
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"key": col,
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"name": name,
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"type": "bar",
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"axis": "primary"
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})
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# 次轴折线图
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for col, name in zip(line_columns, line_names):
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series_config.append({
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"key": col,
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"name": name,
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"type": "line",
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"axis": "secondary"
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})
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# 调用统一的 API
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return create_combo_chart(
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slide, df, categories_col, series_config, position, size
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)
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