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 # Constants SPLIT_TIME = "17:30" BUSINESS_START = "09:30" BUSINESS_END = "01:30" BORDER_COLOR = 'grey' # Changed to grey for the new border DATE_COLOR = '#A9A9A9' A5_WIDTH_IN = 5.83 A5_HEIGHT_IN = 8.27 NUM_COLS = 3 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') # 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): """创建精确的 A5 表格打印布局 (PNG 和 PDF)""" if data.empty: return None # --- 内部绘图函数 --- def generate_figure(): # --- 1. 计算布局和字体大小 --- total_items = len(data) num_rows = math.ceil(total_items / NUM_COLS) if total_items > 0 else 1 # 定义日期标题行的高度（英寸），数据行将填充剩余空间 date_header_height_in = 0.3 data_area_height_in = A5_HEIGHT_IN - date_header_height_in # 计算每个数据单元格的尺寸（英寸） cell_width_in = A5_WIDTH_IN / NUM_COLS cell_height_in = data_area_height_in / num_rows # 将单元格宽度转换为点（1 英寸 = 72 点） cell_width_pt = cell_width_in * 72 cell_height_pt = cell_height_in * 72 # --- 动态字体大小计算 --- # 目标：文本总宽度为单元格宽度的 90% target_text_width_pt = cell_width_pt * 0.9 # 启发式估算：假设最长文本为 "10 23:59" (8个字符)，平均字符宽度约为字体大小的0.6倍 # FONT_SIZE = target_width / (num_chars * avg_char_width_factor) fontsize_from_width = target_text_width_pt / (8 * 0.6) # 字体高度不能超过单元格高度（留出20%的垂直边距） fontsize_from_height = cell_height_pt * 0.8 # 选择两者中较小的一个，以确保文本能完全容纳 base_fontsize = min(fontsize_from_width, fontsize_from_height) # --- 2. 创建图形和网格 --- fig = plt.figure(figsize=(A5_WIDTH_IN, A5_HEIGHT_IN), dpi=300) # 设置无边距，让网格填满整个图纸 fig.subplots_adjust(left=0, right=1, top=1, bottom=0) # 设置字体 plt.rcParams['font.family'] = 'sans-serif' plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 确保字体可用 # 创建网格，顶部为日期行，下方为数据行 # 使用高度（英寸）作为比率，GridSpec会自动归一化 gs = gridspec.GridSpec( num_rows + 1, NUM_COLS, hspace=0, wspace=0, # 无单元格间距 height_ratios=[date_header_height_in] + [cell_height_in] * num_rows, figure=fig ) # --- 3. 补全和排序数据 --- 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 # --- 4. 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 # +1 因为日期行占了第0行 col_grid = idx % NUM_COLS ax = fig.add_subplot(gs[row_grid, col_grid]) # --- 设置点状虚线边框 --- for spine in ax.spines.values(): spine.set_visible(True) spine.set_linestyle((0, (1, 2))) # 点状线: (offset, (on_length, off_length)) spine.set_color(BORDER_COLOR) spine.set_linewidth(0.75) # 点状线可能需要稍粗一点才清晰 # 绘制居中对齐的文本 display_text = f"{hall}{end_time}" ax.text(0.5, 0.5, display_text, fontsize=base_fontsize, fontweight='bold', ha='center', va='center', transform=ax.transAxes) ax.set_xticks([]) ax.set_yticks([]) ax.set_facecolor('none') # --- 5. 绘制日期标题 --- ax_date = fig.add_subplot(gs[0, :]) ax_date.text(0.01, 0.5, f"{date_str} {title}", fontsize=base_fontsize * 0.5, # 日期字体稍小 color=DATE_COLOR, fontweight='bold', ha='left', va='center', transform=ax_date.transAxes) ax_date.set_axis_off() # 完全隐藏日期行的边框和刻度 ax_date.set_facecolor('none') return fig # --- 生成并保存图形 --- fig_for_output = generate_figure() # 保存为 PNG png_buffer = io.BytesIO() fig_for_output.savefig(png_buffer, format='png') # 无需 bbox_inches='tight' png_buffer.seek(0) png_base64 = base64.b64encode(png_buffer.getvalue()).decode() # 保存为 PDF pdf_buffer = io.BytesIO() with PdfPages(pdf_buffer) as pdf: pdf.savefig(fig_for_output) # 无需 bbox_inches='tight' pdf_buffer.seek(0) pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode() plt.close(fig_for_output) # 关闭图形，释放内存 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: # 生成包含 PNG 和 PDF 的字典 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")