## --------------------------------------------------------------------------- ## Licensed to the Apache Software Foundation (ASF) under one or more ## contributor license agreements. See the NOTICE file distributed with ## this work for additional information regarding copyright ownership. ## The ASF licenses this file to You under the Apache License, Version 2.0 ## (the "License"); you may not use this file except in compliance with ## the License. You may obtain a copy of the License at ## ## http://www.apache.org/licenses/LICENSE-2.0 ## ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. ## --------------------------------------------------------------------------- import numpy import pandas as pd from youdao_translator_api import translator sessions = pd.read_excel('ApacheConSessions.xlsx') tracks_dictionary = { "Integration | 集成": "integration", "Integration": "integration", "Workflow/DataProcessing | 工作流/数据处理": "workflowdatagovernance", "Workflow/DataProcessing": "workflowdatagovernance", "Web Server/Tomcat | Web服务": "webserverandtomcat", "Web Server/Tomcat": "webserverandtomcat", "Streaming | 数据流": "streaming", "Streaming": "streaming", "Observability | 可观测性": "observability", "Observability": "observability", "Middleware | 中间件": "middleware", "Middleware": "middleware", "Messaging (Pulsar/Kafka/RocketMQ/ActiveMQ etc. ) | 消息": "messaging", "Messaging (Pulsar/Kafka/RocketMQ/ActiveMQ etc. )": "messaging", "IOT and IIOT | 物联网和工业物联网": "iot", "IoT and IIoT": "iot", "Incubator | 孵化器": "incubator", "Incubator": "incubator", "Data Visualization | 数据可视化": "datavisualization", "Data Visualization": "datavisualization", "Culture | 文化": "culture", "Culture": "culture", "Community | 社区": "community", "Community": "community", "Big Data | 大数据": "bigdata", "Big Data": "bigdata", "AI": "ai", "AI | 人工智能": "ai", "API/MicroService | API/微服务框架": "api", "API/Microservice": "api", "RPC | 远程过程调用": "rpc", "RPC": "rpc", "General | 常规": "rpc", "General": "general" } session_types_dictionary = {"中文": "Chinese Session", "英文": "English Session", "Chinese": "Chinese Session", "English": "English Session"} session_types_chinese_dictionary = {"中文": "中文演讲", "英文": "英文演讲", "English": "英文演讲", "Chinese": "中文演讲"} column_name_transform_dict = { "translator_type": "翻译语言", "type": "演讲语言", "track": "Track", "title": "演讲题目", "status": "审核状态", "abstract": "内容摘要", "speaker_name": "讲师{}-姓名", "speaker_company": "讲师{}-单位名称", "speaker_title": "讲师{}-职称", "speaker_bios": "讲师{}-简介", "time_schedule": "日程安排" } trans_cn_code = "zh-CHS" trans_en_code = "en" session_success_status = "审核通过" translator_type_zh = "中文" translator_type_en = "英文" speaker_data = '{0}: {1}, {2}, {3}' no_speaker_flag = "无" def write_origin_file(file, file_name, mate_data, abstract, speakers_arr): file.write(mate_data) file.write(abstract) file.write("\n ### Speakers: \n ") head_img = '' for index, speaker in enumerate(speakers_arr): if index == 0: head_img_name = file_name.split("-")[1]+".png" else: head_img_name = file_name.split("-")[1] + "_" + str(index+1) + ".png" file.write(head_img.format(head_img_name)) file.write("
") file.write(speaker_data.format(speaker[0], speaker[1], speaker[2], speaker[3])) file.write("\r\n ") file.close() def write_translate_file(file, file_name, mate_data, abstract, speakers_arr, trans_from, trans_to): file.write(mate_data) abstract = translator(abstract, trans_from, trans_to) file.write(abstract) file.write("\n ### Speakers: \n ") head_img = '' for index, speaker in enumerate(speakers_arr): name = translator(speaker[0], trans_from, trans_to) company = translator(speaker[1], trans_from, trans_to) title = translator(speaker[2], trans_from, trans_to) bios = translator(speaker[3], trans_from, trans_to) if index == 0: head_img_name = file_name.split("-")[1] + ".png" else: head_img_name = file_name.split("-")[1] + "_" + str(index + 1) + ".png" file.write(head_img.format(head_img_name)) file.write("
") file.write(speaker_data.format(name, company, title, bios)) file.write("\r\n ") file.close() for index in sessions.index: session_status = sessions.at[index, column_name_transform_dict.get("status")] if session_status == session_success_status: translator_type = sessions.at[index, column_name_transform_dict.get("translator_type")] session_type = sessions.at[index, column_name_transform_dict.get("type")] track_name = sessions.at[index, column_name_transform_dict.get("track")] track_name = tracks_dictionary.get(track_name) session_title = sessions.at[index, column_name_transform_dict.get("title")] session_abstract = sessions.at[index, column_name_transform_dict.get("abstract")] speakers_arr = [] for no in range(1, 4): speaker_name_key = column_name_transform_dict.get("speaker_name").format(str(no)) speaker_name = sessions.at[index, speaker_name_key] if pd.isnull(speaker_name) or speaker_name == no_speaker_flag: continue speaker_company_key = column_name_transform_dict.get("speaker_company").format(str(no)) speaker_company = sessions.at[index, speaker_company_key] speaker_title_key = column_name_transform_dict.get("speaker_title").format(str(no)) speaker_title = sessions.at[index, speaker_title_key] speaker_bios_key = column_name_transform_dict.get("speaker_bios").format(str(no)) speaker_bios = sessions.at[index, speaker_bios_key] speakers_arr.append([speaker_name, speaker_company, speaker_title, speaker_bios]) # print(speakers_arr) # create the markdown file print(session_title) file_name = str(index + 1000) file_name = track_name + "-" + file_name markdown_en_file = open(file_name + ".md", "w+", encoding="utf8") markdown_zh_file = open(file_name + ".zh.md", "w+", encoding="utf8") mate_data = '---\ntitle: "{}"\ndate: "{}" \ntrack: "{}"\npresenters: "{}"\nstype: "{}"\n---\n' schedule_time = "" if not pd.isnull(sessions.at[index, column_name_transform_dict.get("time_schedule")]): schedule_time = sessions.at[index, column_name_transform_dict.get("time_schedule")] speakers_arr = numpy.array(speakers_arr) speakers = speakers_arr[:, 0] speakers = ",".join(speakers) if translator_type == translator_type_en: session_type_en = session_types_dictionary.get(session_type) mate_data_str = mate_data.format(session_title, schedule_time, track_name, speakers, session_type_en) write_origin_file(markdown_en_file, file_name, mate_data_str, session_abstract, speakers_arr) # Translate English to Chinese session_title_cn = translator(session_title, trans_en_code, trans_cn_code) session_type_cn = session_types_chinese_dictionary.get(session_type) mate_data_str_cn = mate_data.format(session_title_cn, schedule_time, track_name, speakers, session_type_cn) write_translate_file(markdown_zh_file, file_name, mate_data_str_cn, session_abstract, speakers_arr, trans_en_code, trans_cn_code) elif translator_type == translator_type_zh: session_type_cn = session_types_chinese_dictionary.get(session_type) mate_data_str = mate_data.format(session_title, schedule_time, track_name, speakers, session_type_cn) write_origin_file(markdown_zh_file, file_name, mate_data_str, session_abstract, speakers_arr) # Translate Chinese to English session_type_en = session_types_dictionary.get(session_type) session_title_en = translator(session_title, trans_cn_code, trans_en_code) mate_data_str_en = mate_data.format(session_title_en, schedule_time, track_name, speakers, session_type_en) write_translate_file(markdown_en_file, file_name, mate_data_str_en, session_abstract, speakers_arr, trans_cn_code, trans_en_code)