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Author SHA1 Message Date
4986a0d617 0.1.200 2026-01-11 03:45:44 +08:00
a65f06332e 0.1.199 2026-01-08 14:19:45 +08:00
b8fea19fc3 0.1.198 2026-01-08 12:19:44 +08:00
fd43e303fa 0.1.196 2026-01-05 16:08:34 +08:00
e75bf4d5f4 0.1.195 2025-12-23 15:42:55 +08:00
2a65f6f639 0.1.194 2025-12-19 12:15:54 +08:00
579a575087 0.1.194 2025-12-19 12:15:03 +08:00
d1df40c1cd 0.1.193 2025-12-15 15:16:26 +08:00
7bcf9f0c87 0.1.192 2025-12-08 12:46:30 +08:00
01a35075d2 0.1.191 2025-12-08 12:14:47 +08:00
6 changed files with 281 additions and 6 deletions

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@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.1.190
version = 0.1.200
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

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@@ -1,6 +1,6 @@
Metadata-Version: 2.4
Name: guan
Version: 0.1.190
Version: 0.1.200
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

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@@ -218,6 +218,125 @@ def langchain_chat_with_memory(prompt="你好", model="qwen-plus", temperature=0
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

View File

@@ -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)]

View File

@@ -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):

View File

@@ -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