import pandas as pd import streamlit as st from datetime import datetime, timedelta import matplotlib.pyplot as plt import io import base64 import matplotlib.gridspec as gridspec import math import re SPLIT_TIME = "17:30" BUSINESS_START = "09:30" BUSINESS_END = "01:30" BORDER_COLOR = '#A9A9A9' DATE_COLOR = '#A9A9A9' def process_schedule(file): """处理上传的 Excel 文件，生成排序和分组后的打印内容""" try: # 读取 Excel，跳过前 8 行 df = pd.read_excel(file, skiprows=8) # 提取所需列 (G9, H9, J9) df = df.iloc[:, [6, 7, 9]] # G, H, J 列 df.columns = ['Hall', 'StartTime', 'EndTime'] # 清理数据 df = df.dropna(subset=['Hall', 'StartTime', 'EndTime']) # 转换影厅格式为带LaTeX上标井号 df['Hall'] = df['Hall'].str.extract(r'(\d+)号').astype(str) + r'$^{\#}$' # 保存原始时间字符串用于诊断 df['original_end'] = df['EndTime'] # 转换时间为 datetime 对象 base_date = datetime.today().date() df['StartTime'] = pd.to_datetime(df['StartTime']) df['EndTime'] = pd.to_datetime(df['EndTime']) # 设置基准时间 business_start = datetime.strptime(f"{base_date} {BUSINESS_START}", "%Y-%m-%d %H:%M") business_end = datetime.strptime(f"{base_date} {BUSINESS_END}", "%Y-%m-%d %H:%M") # 处理跨天情况 if business_end < business_start: business_end += timedelta(days=1) # 标准化所有时间到同一天 for idx, row in df.iterrows(): end_time = row['EndTime'] if end_time.hour < 9: df.at[idx, 'EndTime'] = end_time + timedelta(days=1) if row['StartTime'].hour >= 21 and end_time.hour < 9: df.at[idx, 'EndTime'] = end_time + timedelta(days=1) # 筛选营业时间内的场次 df['time_for_comparison'] = df['EndTime'].apply( lambda x: datetime.combine(base_date, x.time()) ) df.loc[df['time_for_comparison'].dt.hour < 9, 'time_for_comparison'] += timedelta(days=1) valid_times = ( ((df['time_for_comparison'] >= datetime.combine(base_date, business_start.time())) & (df['time_for_comparison'] <= datetime.combine(base_date + timedelta(days=1), business_end.time())) ) df = df[valid_times] # 按散场时间排序 df = df.sort_values('EndTime') # 分割数据 split_time = datetime.strptime(f"{base_date} {SPLIT_TIME}", "%Y-%m-%d %H:%M") split_time_for_comparison = df['time_for_comparison'].apply( lambda x: datetime.combine(base_date, split_time.time()) ) part1 = df[df['time_for_comparison'] <= split_time_for_comparison].copy() part2 = df[df['time_for_comparison'] > split_time_for_comparison].copy() # 格式化时间显示 for part in [part1, part2]: part['EndTime'] = part['EndTime'].dt.strftime('%-I:%M') # 读取日期信息 date_df = pd.read_excel( file, skiprows=5, nrows=1, usecols=[2], header=None ) date_cell = date_df.iloc[0, 0] try: if isinstance(date_cell, str): date_str = datetime.strptime(date_cell, '%Y-%m-%d').strftime('%Y-%m-%d') else: date_str = pd.to_datetime(date_cell).strftime('%Y-%m-%d') except: date_str = datetime.today().strftime('%Y-%m-%d') return part1[['Hall', 'EndTime']], part2[['Hall', 'EndTime']], date_str except Exception as e: st.error(f"处理文件时出错: {str(e)}") return None, None, None def create_print_layout(data, title, date_str): """创建打印布局""" if data.empty: return None # 设置 A5 纸张横向尺寸 fig = plt.figure(figsize=(5.83, 8.27), dpi=300) plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05) # 设置字体 plt.rcParams['font.family'] = 'sans-serif' plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 计算行数和总数 total_items = len(data) num_cols = 3 num_rows = math.ceil(total_items / num_cols) # 创建网格（优化间距参数） gs = gridspec.GridSpec(num_rows + 1, num_cols, hspace=0.2, wspace=0.2, # 增加行列间距 height_ratios=[1.2] * num_rows + [0.2]) # 计算最大字符数 max_char_count = 0 for hall, end_time in data.values: clean_hall = re.sub(r'\$.*?\$', '#', hall) clean_text = f"{clean_hall}{end_time}" current_count = len(clean_text) max_char_count = max(max_char_count, current_count) # 动态计算基础字号（优化计算参数） cell_width_inches = 5.83 / 3 # 每列宽度（A5横向） available_width = cell_width_inches * 0.65 * 72 # 可用宽度减少到65% avg_char_width = 0.8 # 加粗字体宽度系数 base_fontsize = available_width / (max_char_count * avg_char_width) base_fontsize = min(26, base_fontsize) # 设置最大字号限制 # 填充数据 data_values = data.values.tolist() while len(data_values) % 3 != 0: data_values.append(['', '']) sorted_data = [['', '']] * len(data_values) for i, item in enumerate(data_values): if item[0] and item[1]: row = i % math.ceil(len(data_values)/3) col = i // math.ceil(len(data_values)/3) new_index = row * 3 + col if new_index < len(sorted_data): sorted_data[new_index] = item for idx, (hall, end_time) in enumerate(sorted_data): if hall and end_time: row = idx // 3 col = idx % 3 ax = plt.subplot(gs[row, col]) # 设置单元格边界范围（增加内边距） ax.set_xlim(0.1, 0.9) # 左右各留10%边距 ax.set_ylim(0.1, 0.9) # 上下各留10%边距 for spine in ax.spines.values(): spine.set_color(BORDER_COLOR) spine.set_linewidth(0.5) # 添加文本（字号缩小5%） ax.text(0.5, 0.5, f"{hall}{end_time}", fontsize=base_fontsize*0.95, fontweight='bold', ha='center', va='center', transform=ax.transAxes) ax.set_xticks([]) ax.set_yticks([]) # 添加日期信息 ax_date = plt.subplot(gs[0, 0]) ax_date.text(0.05, 0.95, f"{date_str} {title}", fontsize=base_fontsize * 0.4, color=DATE_COLOR, fontweight='bold', ha='left', va='top') for spine in ax_date.spines.values(): spine.set_visible(False) ax_date.set_xticks([]) ax_date.set_yticks([]) # 转换为图片 buffer = io.BytesIO() plt.savefig(buffer, format='png', bbox_inches='tight', pad_inches=0.05) buffer.seek(0) image_base64 = base64.b64encode(buffer.getvalue()).decode() plt.close() return f'data:image/png;base64,{image_base64}' # Streamlit 界面 st.set_page_config(page_title="散厅时间快捷打印", layout="wide") st.title("散厅时间快捷打印") uploaded_file = st.file_uploader("上传【放映场次核对表.xls】文件", type=["xls", "xlsx"]) if uploaded_file: part1, part2, date_str = process_schedule(uploaded_file) if part1 is not None and part2 is not None: part1_image = create_print_layout(part1, "A", date_str) part2_image = create_print_layout(part2, "C", date_str) col1, col2 = st.columns(2) with col1: st.subheader("白班散场预览（时间 ≤ 17:30）") if part1_image: st.image(part1_image) else: st.info("白班部分没有数据") with col2: st.subheader("夜班散场预览（时间 > 17:30）") if part2_image: st.image(part2_image) else: st.info("夜班部分没有数据")