0.1.200

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.188
+version = 0.1.200
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.4
 Name: guan
-Version: 0.1.188
+Version: 0.1.200
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/AI_chat.py

@@ -1,6 +1,6 @@
 # Module: AI_chat
 
-# AI 对话
+# AI 对话（当前默认模型为 'hunyuan-lite'，无记忆）
 def chat(prompt='你好', model=1, stream=1, stream_label=0):
     import requests
     url = "http://api.guanjihuan.com/chat"
@@ -27,7 +27,7 @@ def chat(prompt='你好', model=1, stream=1, stream_label=0):
             print('\n--- End Chat Stream Message ---\n')
     return response
 
-# 加上函数代码的 AI 对话
+# 加上函数代码的 AI 对话（当前默认模型为 'hunyuan-lite'，无记忆）
 def chat_with_function_code(function_name, prompt='', model=1, stream=1):
     import guan
     function_source = guan.get_source(function_name)
@@ -37,7 +37,7 @@ def chat_with_function_code(function_name, prompt='', model=1, stream=1):
         response = guan.chat(prompt=function_source+'\n\n'+prompt, model=model, stream=stream)
     return response
 
-# 机器人自动对话
+# 机器人自动对话（当前默认模型为 'hunyuan-lite'，无记忆）
 def auto_chat(prompt='你好', round=2, model=1, stream=1):
     import guan
     response0 = prompt
@@ -48,7 +48,7 @@ def auto_chat(prompt='你好', round=2, model=1, stream=1):
         print('机器人 2: ')
         response0 = guan.chat(prompt=response1, model=model, stream=stream)
 
-# 机器人自动对话（引导对话）
+# 机器人自动对话（引导对话）（当前默认模型为 'hunyuan-lite'，无记忆）
 def auto_chat_with_guide(prompt='你好', guide_message='（回答字数少于30个字，最后反问我一个问题）', round=5, model=1, stream=1):
     import guan
     response0 = prompt
@@ -59,8 +59,46 @@ def auto_chat_with_guide(prompt='你好', guide_message='（回答字数少于30
         print('机器人 2: ')
         response0 = guan.chat(prompt=response1+guide_message, model=model, stream=stream)
 
-# 使用 LangChain 无记忆对话（需要 API Key)
+# 使用 OpenAI 框架对话（需要 API Key)
-def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_message=None, print_show=1, load_env=1):
+def openai_chat(prompt="你好", model="qwen-plus", temperature=0.7, system_message=None, history=[], print_show=1, load_env=1):
+    import os
+    from openai import OpenAI
+    if load_env:
+        import dotenv
+        from pathlib import Path
+        import inspect
+        caller_frame = inspect.stack()[1]
+        caller_dir = Path(caller_frame.filename).parent
+        env_path = caller_dir / ".env"
+        if env_path.exists():
+            dotenv.load_dotenv(env_path)
+    client = OpenAI(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        base_url=os.getenv("DASHSCOPE_BASE_URL"),
+    )
+    if system_message == None:
+        messages = history+[{"role": "user", "content": prompt}]
+    else:
+        messages = [{"role": "system", "content": system_message}]+history+[{"role": "user", "content": prompt}]
+    completion = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            stream=True,
+        )
+    response = ''
+    for chunk in completion:
+        response += chunk.choices[0].delta.content
+        if print_show:
+            print(chunk.choices[0].delta.content, end="", flush=True)
+    if print_show:
+        print()
+    history.append({"role": "user", "content": prompt})
+    history.append({"role": "assistant", "content": response})
+    return response, history
+
+# 通过 LangChain 加载模型（需要 API Key)
+def load_langchain_model(model="qwen-plus", temperature=0.7, load_env=1):
     from langchain_openai import ChatOpenAI
     from langchain_core.prompts import ChatPromptTemplate
     import os
@@ -76,7 +114,30 @@ def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_messa
     llm = ChatOpenAI(
         api_key=os.getenv("OPENAI_API_KEY"),
         base_url=os.getenv("DASHSCOPE_BASE_URL"),
-        model="qwen-plus",
+        model=model,
+        temperature=temperature,
+        streaming=True,
+    )
+    return llm
+
+# 使用 LangChain 无记忆对话（需要 API Key)
+def langchain_chat_without_memory(prompt="你好", model="qwen-plus", temperature=0.7, system_message=None, print_show=1, load_env=1):
+    from langchain_openai import ChatOpenAI
+    from langchain_core.prompts import ChatPromptTemplate
+    import os
+    if load_env:
+        import dotenv
+        from pathlib import Path
+        import inspect
+        caller_frame = inspect.stack()[1]
+        caller_dir = Path(caller_frame.filename).parent
+        env_path = caller_dir / ".env"
+        if env_path.exists():
+            dotenv.load_dotenv(env_path)
+    llm = ChatOpenAI(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        base_url=os.getenv("DASHSCOPE_BASE_URL"),
+        model=model,
         temperature=temperature,
         streaming=True,
     )
@@ -100,7 +161,7 @@ def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_messa
     return response
 
 # 使用 LangChain 有记忆对话（记忆临时保存在函数的属性上，需要 API Key)
-def langchain_chat_with_memory(prompt="你好", temperature=0.7, system_message=None, session_id="default", print_show=1, load_env=1):
+def langchain_chat_with_memory(prompt="你好", model="qwen-plus", temperature=0.7, system_message=None, session_id="default", print_show=1, load_env=1):
     from langchain_openai import ChatOpenAI
     from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
     from langchain_core.runnables.history import RunnableWithMessageHistory
@@ -118,7 +179,7 @@ def langchain_chat_with_memory(prompt="你好", temperature=0.7, system_message=
     llm = ChatOpenAI(
         api_key=os.getenv("OPENAI_API_KEY"),
         base_url=os.getenv("DASHSCOPE_BASE_URL"),
-        model="qwen-plus",
+        model=model,
         temperature=temperature,
         streaming=True,
     )
@@ -157,6 +218,125 @@ def langchain_chat_with_memory(prompt="你好", temperature=0.7, system_message=
         print()
     return response
 
+# 使用 LangChain 调用工具对话（需要 API Key)
+def langchain_chat_with_tools(prompt="你好", model="qwen-plus", temperature=0.7, system_message=None, tools=None, print_show=1, load_env=1):
+    import guan
+    if tools==None:
+        response = guan.langchain_chat_without_memory(prompt=prompt, model=model, temperature=temperature, system_message=system_message, print_show=print_show, load_env=load_env)
+    else:
+        import os
+        from langchain_openai import ChatOpenAI
+        from langchain_core.prompts import ChatPromptTemplate
+        from langchain.agents import create_openai_tools_agent, AgentExecutor
+        if load_env:
+            import dotenv
+            from pathlib import Path
+            import inspect
+            caller_frame = inspect.stack()[1]
+            caller_dir = Path(caller_frame.filename).parent
+            env_path = caller_dir / ".env"
+            if env_path.exists():
+                dotenv.load_dotenv(env_path)
+        llm = ChatOpenAI(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            base_url=os.getenv("DASHSCOPE_BASE_URL"),
+            model=model,
+            temperature=temperature,
+            streaming=False,
+        )
+        if system_message == None:
+            prompt_template = ChatPromptTemplate.from_messages([
+            ("human", "{input_message}"),
+            ("placeholder", "{agent_scratchpad}"),
+        ])
+        else:
+            prompt_template = ChatPromptTemplate.from_messages([
+                ("system", system_message),
+                ("human", "{input_message}"),
+                ("placeholder", "{agent_scratchpad}"),
+            ])
+        agent = create_openai_tools_agent(llm, tools, prompt_template)
+        agent_executor = AgentExecutor(
+            agent=agent,
+            tools=tools,
+            verbose=bool(print_show),
+            handle_parsing_errors=True,
+        )
+        response_result = agent_executor.invoke({"input_message": prompt})
+        response = response_result["output"]
+        if print_show:
+            print('\n'+response)
+    return response
+
+# 使用 LangChain 调用工具有记忆对话（记忆临时保存在函数的属性上，需要 API Key)
+def langchain_chat_with_tools_and_memory(prompt="你好", model="qwen-plus", temperature=0.7, system_message=None, tools=None, session_id="default", print_show=1, load_env=1):
+    import guan
+    if tools==None:
+        response = guan.langchain_chat_with_memory(prompt=prompt, model=model, temperature=temperature, system_message=system_message, session_id=session_id, print_show=print_show, load_env=load_env)
+    else:
+        import os
+        from langchain_openai import ChatOpenAI
+        from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+        from langchain_core.runnables.history import RunnableWithMessageHistory
+        from langchain_community.chat_message_histories import ChatMessageHistory
+        from langchain.agents import create_openai_tools_agent, AgentExecutor
+        if load_env:
+            import dotenv
+            from pathlib import Path
+            import inspect
+            caller_frame = inspect.stack()[1]
+            caller_dir = Path(caller_frame.filename).parent
+            env_path = caller_dir / ".env"
+            if env_path.exists():
+                dotenv.load_dotenv(env_path)
+        llm = ChatOpenAI(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            base_url=os.getenv("DASHSCOPE_BASE_URL"),
+            model=model,
+            temperature=temperature,
+            streaming=False,
+        )
+        if system_message == None:
+            prompt_template = ChatPromptTemplate.from_messages([
+            MessagesPlaceholder("history"),
+            ("human", "{input_message}"),
+            ("placeholder", "{agent_scratchpad}"),
+        ])
+        else:
+            prompt_template = ChatPromptTemplate.from_messages([
+                ("system", system_message),
+                MessagesPlaceholder("history"),
+                ("human", "{input_message}"),
+                ("placeholder", "{agent_scratchpad}"),
+            ])
+        
+        if not hasattr(langchain_chat_with_tools_and_memory, "store"):
+            langchain_chat_with_tools_and_memory.store = {}
+        
+        def get_session_history(sid: str):
+            if sid not in langchain_chat_with_tools_and_memory.store:
+                langchain_chat_with_tools_and_memory.store[sid] = ChatMessageHistory()
+            return langchain_chat_with_tools_and_memory.store[sid]
+        
+        agent = create_openai_tools_agent(llm, tools, prompt_template)
+        agent_executor = AgentExecutor(
+            agent=agent,
+            tools=tools,
+            verbose=bool(print_show),
+            handle_parsing_errors=True,
+        )
+        agent_with_chat_history = RunnableWithMessageHistory(
+            agent_executor,
+            get_session_history,
+            input_messages_key="input_message",
+            history_messages_key="history",
+        )
+        response_result = agent_with_chat_history.invoke({"input_message": prompt}, config={"configurable": {"session_id": session_id}})
+        response = response_result["output"]
+        if print_show:
+            print('\n'+response)
+    return response
+
 # 使用 Ollama 本地模型对话（需要运行 Ollama 和下载对应的模型）
 def ollama_chat(prompt='你好/no_think', model="qwen3:0.6b", temperature=0.8, print_show=1):
     import ollama
@@ -181,14 +361,14 @@ def ollama_chat(prompt='你好/no_think', model="qwen3:0.6b", temperature=0.8, p
         print()
     return response
 
-# ModelScope 加载本地模型和分词器（只加载一次）
+# ModelScope 加载本地模型和分词器（只加载一次，需要有模型文件）
 def load_modelscope_model(model_name="D:/models/Qwen/Qwen3-0.6B"):
     from modelscope import AutoModelForCausalLM, AutoTokenizer
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     return model, tokenizer
 
-# 使用 ModelScope 本地模型聊天
+# 使用 ModelScope 本地模型聊天（需要有模型文件）
 def modelscope_chat(model, tokenizer, prompt='你好 /no_think', history=[], temperature=0.7, top_p=0.8, print_show=1):
     from threading import Thread
     from transformers import TextIteratorStreamer
@@ -218,13 +398,13 @@ def modelscope_chat(model, tokenizer, prompt='你好 /no_think', history=[], tem
         response += new_text
     if print_show:
         print()
-    new_history = history + [
+    history += [
         {"role": "user", "content": prompt},
         {"role": "assistant", "content": response}
     ]
-    return response, new_history
+    return response, history
 
-# LLaMA 加载本地模型（只加载一次）
+# LLaMA 加载本地模型（只加载一次，需要有模型文件）
 def load_llama_model(model_path="D:/models/Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf"):
     from llama_cpp import Llama
     llm = Llama(
@@ -236,11 +416,11 @@ def load_llama_model(model_path="D:/models/Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.
     )
     return llm
 
-# 使用 LLaMA 本地模型聊天
+# 使用 LLaMA 本地模型聊天（需要有模型文件）
 def llama_chat(llm, prompt='你好 /no_think', history=[], temperature=0.7, top_p=0.8, print_show=1):
-    new_history = history + [{"role": "user", "content": prompt}]
+    history += [{"role": "user", "content": prompt}]
     llm_response = llm.create_chat_completion(
-        messages=new_history,
+        messages=history,
         temperature=temperature,
         top_p=top_p,
         repeat_penalty=1.5,
@@ -256,5 +436,5 @@ def llama_chat(llm, prompt='你好 /no_think', history=[], temperature=0.7, top_
                 print(token, end="", flush=True)
     if print_show:
         print()
-    new_history.append({"role": "assistant", "content": response})
+    history.append({"role": "assistant", "content": response})
-    return response, new_history
+    return response, history

PyPI/src/guan/data_processing.py

@@ -11,6 +11,16 @@ def logging_with_day_and_time(content='', filename='time_logging', file_format='
        else:
            f2.write(datetime_today+' '+datetime_time+' '+str(content)+'\n')
 
+# 获取当前位置的 Unix 时间戳，并打印某段程序的运行时间
+def record_time_and_print_running_time(start_time=None):
+    import time
+    current_time = time.time()
+    if start_time == None:
+      print("\n--- 开始计时（第一个记录点）---\n")
+    else:
+      print(f"\n--- 自上一个记录点已运行: {current_time - start_time:.2f} 秒 ---\n")
+    return current_time
+
 # 使用该函数运行某个函数并获取函数计算时间（秒）
 def timer(function_name, *args, **kwargs):
     import time
@@ -80,6 +90,69 @@ def loop_calculation_with_three_parameters(function_name, parameter_array_1, par
         i1 += 1
     return result_array
 
+# 文本对比
+def word_diff(a, b, print_show=1):
+    import difflib
+    import jieba
+    import logging
+    jieba.setLogLevel(logging.ERROR)
+    a_words = jieba.lcut(a)
+    b_words = jieba.lcut(b)
+    sm = difflib.SequenceMatcher(None, a_words, b_words, autojunk=False)
+    result = []
+    for tag, i1, i2, j1, j2 in sm.get_opcodes():
+        if tag == "equal":
+            result.extend(a_words[i1:i2])
+        elif tag == "delete":
+            result.append("\033[9;91m" + "".join(a_words[i1:i2]) + "\033[0m")
+        elif tag == "insert":
+            result.append("\033[92m" + "".join(b_words[j1:j2]) + "\033[0m")
+        elif tag == "replace":
+            result.append("\033[9;91m" + "".join(a_words[i1:i2]) + "\033[0m")
+            result.append(" ")
+            result.append("\033[92m" + "".join(b_words[j1:j2]) + "\033[0m")
+    diff_result = "".join(result)
+    if print_show:
+        print(diff_result)
+    return diff_result
+
+# 文本对比（写入HTML文件）
+def word_diff_to_html(a, b, filename='diff_result', write_file=1):
+    import difflib
+    from html import escape
+    import jieba
+    import logging
+    jieba.setLogLevel(logging.ERROR)
+    a_words = jieba.lcut(a)
+    b_words = jieba.lcut(b)
+    sm = difflib.SequenceMatcher(None, a_words, b_words, autojunk=False)
+    html_parts = []
+    for tag, i1, i2, j1, j2 in sm.get_opcodes():
+        if tag == "equal":
+            html_parts.append("".join(map(escape, a_words[i1:i2])))
+        elif tag == "delete":
+            html_parts.append(f"<span style='background:#e74c3c;color:white;padding:1px 2px;border-radius:2px;text-decoration:line-through;'>"
+                        + "".join(map(escape, a_words[i1:i2]))
+                        + "</span>")
+        elif tag == "insert":
+            html_parts.append(f"<span style='background:#2ecc71;color:white;padding:1px 2px;border-radius:2px;'>"
+                        + "".join(map(escape, b_words[j1:j2]))
+                        + "</span>")
+        elif tag == "replace":
+            html_parts.append(f"<span style='background:#e74c3c;color:white;padding:1px 2px;border-radius:2px;text-decoration:line-through;'>"
+                        + "".join(map(escape, a_words[i1:i2]))
+                        + "</span>")
+            html_parts.append(" ")
+            html_parts.append(f"<span style='background:#2ecc71;color:white;padding:1px 2px;border-radius:2px;'>"
+                        + "".join(map(escape, b_words[j1:j2]))
+                        + "</span>")
+    diff_result = "".join(html_parts)
+    diff_result = diff_result.replace("\n", "<br>")
+    if write_file:
+        with open(filename+'.html', 'w', encoding='UTF-8') as f:
+            f.write(diff_result)
+    return diff_result
+
 # 打印数组
 def print_array(array, line_break=0):
     if line_break == 0:
@@ -168,6 +241,42 @@ def standard_deviation_with_formula(data_array):
     std_result = np.sqrt(averaged_squared_data-averaged_data**2)
     return std_result
 
+# 使用公式计算皮尔逊相关系数
+def calculate_pearson_correlation(x_array, y_array):
+    import numpy as np
+    mean_x = np.mean(x_array)
+    mean_y = np.mean(y_array)
+    numerator = np.sum((x_array - mean_x) * (y_array - mean_y))
+    sum_sq_x = np.sum((x_array - mean_x) ** 2)
+    sum_sq_y = np.sum((y_array - mean_y) ** 2)
+    denominator = np.sqrt(sum_sq_x * sum_sq_y)
+    correlation = numerator / denominator
+    return correlation
+
+# 使用 scipy 计算皮尔逊相关系数和 p 值
+def calculate_pearson_correlation_with_scipy(x_array, y_array):
+    import scipy.stats
+    correlation, p_value = scipy.stats.pearsonr(x_array, y_array)
+    return correlation, p_value
+
+# 使用 scipy 计算多个数组的皮尔逊相关系数和 p 值的矩阵
+def calculate_correlation_matrix_for_multiple_arrays(multiple_arrays):
+    import scipy.stats
+    import numpy as np
+    num_arrays = len(multiple_arrays)
+    correlation_matrix = np.zeros((num_arrays, num_arrays))
+    p_value_matrix = np.zeros((num_arrays, num_arrays))
+    row_idx = 0
+    for array_1 in multiple_arrays:
+        col_idx = 0
+        for array_2 in multiple_arrays:
+            correlation, p_value = scipy.stats.pearsonr(array_1, array_2)
+            correlation_matrix[row_idx, col_idx] = correlation
+            p_value_matrix[row_idx, col_idx] = p_value
+            col_idx += 1
+        row_idx += 1
+    return correlation_matrix, p_value_matrix
+
 # 获取两个模式之间的字符串
 def get_string_between_two_patterns(original_string, start, end, include_start_and_end=0):
     import re
@@ -274,6 +383,14 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
         end = time.time()
         print('Total running time = '+str((end-start)/60)+' min')
 
+# 根据 “.” 和 “。” 符号进行分句
+def split_text_into_sentences(text):
+    import re
+    pattern = r'(?<=[。])|(?<=\.)(?=\s|$)'
+    sentences = re.split(pattern, text)
+    sentence_array = [s.strip() for s in sentences if s.strip()]
+    return sentence_array
+
 # 根据一定的字符长度来分割文本
 def split_text(text, width=100):  
     split_text_list = [text[i:i+width] for i in range(0, len(text), width)]

PyPI/src/guan/file_reading_and_writing.py

@@ -471,7 +471,7 @@ def open_file(filename='a', file_format='.txt', mode='add'):
         f = open(filename+file_format, 'w', encoding='UTF-8')
     return f
 
-# 打印到TXT文件
+# 打印到TXT文件（补充内容）
 def print_to_file(*args, filename='print_result', file_format='.txt', print_on=True):
     if print_on==True:
         for arg in args:
@@ -483,7 +483,12 @@ def print_to_file(*args, filename='print_result', file_format='.txt', print_on=T
     f.write('\n')
     f.close()
 
-# 读取文本文件内容。如果文件不存在，返回空字符串
+# 写入到文本文件（覆盖内容）
+def write_text_file(content, filename='a', file_format='.txt'):
+    with open(filename+file_format, 'w', encoding='UTF-8') as f:
+        f.write(content)
+
+# 读取文本文件内容（如果文件不存在，返回空字符串）
 def read_text_file(file_path='./a.txt', make_file=None):
     import os
     if not os.path.exists(file_path):

PyPI/src/guan/others.py

@@ -28,7 +28,18 @@ def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_fil
     with open(output_file, 'wb') as combined_file:
         output_pdf.write(combined_file)
 
-# 使用pdfminer3k将PDF文件转成文本
+# 使用pdfplumber将PDF文件转成文本
+def pdf_to_text_with_pdfplumber(pdf_path):
+    import pdfplumber
+    with pdfplumber.open(pdf_path) as pdf:
+        all_text = []
+        for page in pdf.pages:
+            text = page.extract_text()
+            all_text.append(text)
+        content = "\n\n".join(all_text)
+        return content
+
+# 使用pdfminer3k将PDF文件转成文本（仅仅支持旧版本的 pdfminer3k）
 def pdf_to_text_with_pdfminer3k(pdf_path):
     from pdfminer.pdfparser import PDFParser, PDFDocument
     from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
@@ -115,6 +126,29 @@ def get_links_from_pdf(pdf_path, link_starting_form=''):
                                 old = u['/A']['/URI']
     return links
 
+# 将某个文件夹中的某个类型的文本文件全部修改为另外一个编码，其他文件不变
+def convert_file_encoding_for_one_directory(source_directory, target_directory, file_formats=['.m'], src_encoding='utf-8', dst_encoding='gb18030'):
+    import os
+    import shutil
+    os.makedirs(target_directory, exist_ok=True)
+    for root, dirs, files in os.walk(source_directory):
+        rel_path = os.path.relpath(root, source_directory) 
+        target_subdir = os.path.join(target_directory, rel_path) if rel_path != '.' else target_directory
+        os.makedirs(target_subdir, exist_ok=True)
+        for file in files:
+            src_file = os.path.join(root, file)
+            dst_file = os.path.join(target_subdir, file)
+            if any(file.lower().endswith(ext.lower()) for ext in file_formats):
+                try:
+                    with open(src_file, 'r', encoding=src_encoding) as f:
+                        content = f.read()
+                    with open(dst_file, 'w', encoding=dst_encoding) as f:
+                        f.write(content)
+                except Exception as e:
+                    shutil.copy2(src_file, dst_file)
+            else:
+                shutil.copy2(src_file, dst_file)
+
 # 获取当前日期字符串
 def get_date(bar=True):
     import datetime
@@ -770,7 +804,7 @@ def creat_qrcode(data="https://www.guanjihuan.com", filename='a', file_format='.
     img = qrcode.make(data)
     img.save(filename+file_format)
 
-# 通过Sci-Hub网站下载文献
+# 通过Sci-Hub网站下载文献（该方法可能失效）
 def download_with_scihub(address=None, num=1):
     from bs4 import BeautifulSoup
     import re