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 from matplotlib.backends.backend_pdf import PdfPages from matplotlib.patches import FancyBboxPatch 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 "#号" 格式 df['Hall'] = df['Hall'].str.extract(r'(\d+)号').astype(str) + ' ' # 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('%-H:%M') # 关键修改：精确读取C6单元格 date_df = pd.read_excel( file, skiprows=5, # 跳过前5行（0-4） nrows=1, # 只读1行 usecols=[2], # 第三列（C列） header=None # 无表头 ) date_cell = date_df.iloc[0, 0] try: # 处理不同日期格式 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 (PNG 和 PDF)，动态调整字体大小""" if data.empty: return None # --- 创建 PNG 图形 --- png_fig = plt.figure(figsize=(5.83, 8.27), dpi=300) # A5 竖向 png_ax_container = png_fig.add_subplot(111) png_ax_container.set_axis_off() png_fig.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02) # --- 创建 PDF 图形 --- pdf_fig = plt.figure(figsize=(5.83, 8.27), dpi=300) # A5 竖向 pdf_ax_container = pdf_fig.add_subplot(111) pdf_ax_container.set_axis_off() pdf_fig.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02) # --- 内部绘图函数 --- def process_figure(fig, is_pdf=False): plt.rcParams['font.family'] = 'sans-serif' plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'SimHei'] # 添加备用中文字体 total_items = len(data) num_cols = 3 num_rows = math.ceil(total_items / num_cols) gs = gridspec.GridSpec(num_rows + 1, num_cols, hspace=0.05, wspace=0.05, height_ratios=[0.1] + [1] * num_rows, figure=fig) # --- 新增：预先计算单元格的目标宽度（以像素为单位）--- target_width_px = 1 if total_items > 0: # 创建一个临时坐标轴来测量其像素宽度 ax_temp = fig.add_subplot(gs[1, 0]) # 必须绘制画布才能使用渲染器并获得几何属性 fig.canvas.draw() # 获取像素宽度并计算目标宽度（90%） target_width_px = ax_temp.get_window_extent().width * 0.90 # 移除临时坐标轴 ax_temp.remove() # --- 预计算结束 --- # 此字体大小计算仅用于顶部的日期 available_height_per_row = (8.27 * 0.9 * (1 / 1.2)) / num_rows if num_rows > 0 else 1 date_fontsize = min(40, max(10, available_height_per_row * 72 * 0.5)) data_values = data.values.tolist() while len(data_values) % num_cols != 0: data_values.append(['', '']) rows_per_col_layout = math.ceil(len(data_values) / num_cols) sorted_data = [['', '']] * len(data_values) for i, item in enumerate(data_values): if item[0] and item[1]: row_in_col = i % rows_per_col_layout col_idx = i // rows_per_col_layout new_index = row_in_col * num_cols + col_idx if new_index < len(sorted_data): sorted_data[new_index] = item # 绘制数据单元格 for idx, (hall, end_time) in enumerate(sorted_data): if hall and end_time: row_grid = idx // num_cols + 1 col_grid = idx % num_cols if row_grid < num_rows + 1: ax = fig.add_subplot(gs[row_grid, col_grid]) for spine in ax.spines.values(): spine.set_visible(False) bbox = FancyBboxPatch( (0.01, 0.01), 0.98, 0.98, boxstyle="round,pad=0,rounding_size=0.02", edgecolor=BORDER_COLOR, facecolor='none', linewidth=0.5, transform=ax.transAxes, clip_on=False ) ax.add_patch(bbox) display_text = f"{hall}{end_time}" # --- 修改部分：动态字体大小调整逻辑 --- # 创建一个文本对象 t = ax.text(0.5, 0.5, display_text, fontweight='bold', ha='center', va='center', transform=ax.transAxes) # 从一个较大的字号开始，迭代查找最佳字号 current_size = 120 # 从更大的字号开始 while current_size > 1: t.set_fontsize(current_size) # 获取渲染后的文本边界框 text_bbox = t.get_window_extent(renderer=fig.canvas.get_renderer()) # 如果文本宽度小于等于目标宽度，则此字号适用 if text_bbox.width <= target_width_px: break current_size -= 2 # 步长可以大一点以提高速度 # --- 修改结束 --- ax.set_xticks([]) ax.set_yticks([]) else: print(f"Warning: Index out of bounds - idx={idx}, row_grid={row_grid}, col_grid={col_grid}") # 添加日期信息 ax_date = fig.add_subplot(gs[0, :]) ax_date.text(0.01, 0.5, f"{date_str} {title}", fontsize=date_fontsize * 0.5, # 日期使用之前计算的字号 color=DATE_COLOR, fontweight='bold', ha='left', va='center', transform=ax_date.transAxes) for spine in ax_date.spines.values(): spine.set_visible(False) ax_date.set_xticks([]) ax_date.set_yticks([]) ax_date.set_facecolor('none') # --- 处理图形 --- process_figure(png_fig) process_figure(pdf_fig, is_pdf=True) # --- 保存 PNG --- png_buffer = io.BytesIO() png_fig.savefig(png_buffer, format='png', bbox_inches='tight', pad_inches=0.02) png_buffer.seek(0) png_base64 = base64.b64encode(png_buffer.getvalue()).decode() plt.close(png_fig) # --- 保存 PDF --- pdf_buffer = io.BytesIO() with PdfPages(pdf_buffer) as pdf: pdf.savefig(pdf_fig, bbox_inches='tight', pad_inches=0.02) pdf_buffer.seek(0) pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode() plt.close(pdf_fig) return { 'png': f'data:image/png;base64,{png_base64}', 'pdf': f'data:application/pdf;base64,{pdf_base64}' } def display_pdf(base64_pdf): """在Streamlit中嵌入显示PDF""" pdf_display = f'' return pdf_display # 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"]) if uploaded_file: part1, part2, date_str = process_schedule(uploaded_file) if part1 is not None and part2 is not None: part1_output = create_print_layout(part1, "A", date_str) part2_output = 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_output: tab1_1, tab1_2 = st.tabs(["PDF 预览", "PNG 预览"]) with tab1_1: st.markdown(display_pdf(part1_output['pdf']), unsafe_allow_html=True) with tab1_2: st.image(part1_output['png']) 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_output: tab2_1, tab2_2 = st.tabs(["PDF 预览", "PNG 预览"]) with tab2_1: st.markdown(display_pdf(part2_output['pdf']), unsafe_allow_html=True) with tab2_2: st.image(part2_output['png']) else: st.info("夜班部分没有数据")