![在这里插入图片描述](/static/img/images/03819.png)






```python
import tkinter as tk
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong']  # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
# 函数用于执行最小二乘法线性拟合
def linear_regression():
    x_values = [float(x) for x in x_entry.get().split()]
    y_values = [float(y) for y in y_entry.get().split()]
    A = np.vstack([x_values, np.ones(len(x_values))]).T
    m, c = np.linalg.lstsq(A, y_values, rcond=None)[0]
    # 清空画布
    ax[0].clear()
    ax[1].clear()
    # 绘制数据点
    ax[0].scatter(x_values, y_values, label='数据点')
    # 绘制最小二乘法拟合线
    ax[0].plot(x_values, m * np.array(x_values) + c, 'r', label='自己计算的')
    ax[0].set_title(f'自己计算的: Y = {m:.5f} * X + {c:.5f}')
    # 从斜率和截距输入框获取备选直线的参数
    lines_m = [float(m) for m in m_entry.get().split()]
    lines_c = [float(c) for c in c_entry.get().split()]
    # 绘制备选直线
    for i in range(len(lines_m)):
        ax[0].plot(x_values, lines_m[i] * np.array(x_values) + lines_c[i], label=f'直线 {i + 1}')
    # 计算备选直线中最好的拟合直线，得到是哪一条直线最好
    best_line_index = np.argmin([np.sum(np.square(y_values - (m * np.array(x_values) + c))) for m, c in
                                 zip(lines_m, lines_c)])
    ax[0].set_xlabel('X')
    ax[0].set_ylabel('Y')
    ax[0].legend()
    # 在ax[1]绘制最好的直线
    ax[1].scatter(x_values, y_values, label='数据点')
    ax[1].plot(x_values, lines_m[best_line_index] * np.array(x_values) + lines_c[best_line_index], 'r',
               label=f'直线 {best_line_index + 1}')
    ax[1].set_title(
        f'最佳备选线: Y = {lines_m[best_line_index]} * X + {lines_c[best_line_index]},Index: {best_line_index + 1}')
    ax[1].set_xlabel('X')
    ax[1].set_ylabel('Y')
    ax[1].legend()
    canvas.draw()
# 创建Tkinter窗口
root = tk.Tk()
root.title('最小二乘法线性拟合')
frame = tk.Frame(root)
frame.pack(padx=10, pady=10)
# 创建输入框和标签
x_label = tk.Label(frame, text='输入x值（用空格分隔）:')
x_label.pack()
x_entry = tk.Entry(frame)
x_entry.insert(0, '2 4 6 8 10')  # 设置默认值
x_entry.pack()
y_label = tk.Label(frame, text='输入y值（用空格分隔）:')
y_label.pack()
y_entry = tk.Entry(frame)
y_entry.insert(0, '10.046 20.090 30.155 40.125 50.074')  # 设置默认值
y_entry.pack()
m_label = tk.Label(frame, text='输入备选直线的斜率（用空格分隔）:')
m_label.pack()
m_entry = tk.Entry(frame)
m_entry.insert(0, '5.0 5.01 4.97 4.95 5.08')  # 设置默认值
m_entry.pack()
c_label = tk.Label(frame, text='输入备选直线的截距（用空格分隔）:')
c_label.pack()
c_entry = tk.Entry(frame)
c_entry.insert(0, '0.08 0.07 0.12 0.15 0.06')  # 设置默认值
c_entry.pack()
# 创建确认按钮
calculate_button = tk.Button(frame, text='确认', command=linear_regression)
calculate_button.pack()
# 创建大画布
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
canvas = FigureCanvasTkAgg(fig, master=frame)
canvas.get_tk_widget().pack()
canvas.draw()
# 运行Tkinter主循环
root.mainloop()
```

最小二乘法，可视化UI界面

![在这里插入图片描述](/static/img/images/03841.png)






python

```python
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse
from typing import List
import requests
app = FastAPI()
# 配置静态文件目录
app.mount("/static", StaticFiles(directory="static"), name="static")
# HTML页面路由
@app.get("/", response_class=HTMLResponse)
async def get_upload_page():
    return """
    <!DOCTYPE html>
    <html>
    <head>
        <title>Image Upload</title>
    </head>
    <body>
        <h1>Image Upload</h1>
        <form method="post" enctype="multipart/form-data">
            <input type="file" name="file">
            <button type="submit">Upload</button>
        </form>
        <div id="descriptions"></div>
        <script src="/static/main.js"></script>
    </body>
    </html>
    """
# 文件上传端点
@app.post("/upload")
async def file_(file: bytes = File(...)):  # 如果要上传多个文件 files: List[bytes] = File(...)
    """使用File类 文件内容会以bytes的形式读入内存 适合于上传小文件"""
    return {"file_size": len(file)}  # 返回文件大小
if __name__ == "__main__":
    import uvicorn
    uvicorn.run('main:app', host='0.0.0.0', port=8000, reload=True, workers=1)
```


js

```js
document.addEventListener('DOMContentLoaded', () => {
  const form = document.querySelector('form');
  const fileInput = document.querySelector('input[type="file"]');
  const descriptions = document.getElementById('descriptions');
  form.addEventListener('submit', async (e) => {
    e.preventDefault();
    const formData = new FormData();
    formData.append('file', fileInput.files[0]);
    try {
      const response = await fetch('/upload', {
        method: 'POST',
        body: formData,
      });
      if (response.ok) {
        const data = await response.json();
        descriptions.innerHTML = `File size: ${data.file_size} bytes`;
      } else {
        descriptions.innerHTML = 'Upload failed';
      }
    } catch (error) {
      console.error('Error:', error);
      descriptions.innerHTML = 'An error occurred';
    }
  });
});
```


结果网页：

![在这里插入图片描述](/static/img/images/03842.png)

【Python Fastapi】js上传文件，fastapi处理，js显示回传信息

我是windows系统，pip install cython_bbox无法编译。





解决：

安装`pip install cpython`

安装VS2019企业版，为了windows能编译一些乱七八糟的，我一般都装个这个。

```shell
key
BF8Y8-GN2QH-T84XB-QVY3B-RC4DF
```

下载源码：`https://github.com/samson-wang/cython_bbox/tree/master`

执行安装：

```
cd C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master
pip install .
```

日志：

```
(py38) C:\Users\xjsd\Downloads\lap-master\lap-master>cd C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master
(py38) C:\Users\xjsd\Downloads\cython_bbox-master\cython_bbox-master>pip install .
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing c:\users\xjsd\downloads\cython_bbox-master\cython_bbox-master
  Preparing metadata (setup.py) ... done
Requirement already satisfied: Cython in c:\users\xjsd\.conda\envs\py38\lib\site-packages (from cython-bbox==0.1.3) (3.0.2)
Requirement already satisfied: numpy in c:\users\xjsd\.conda\envs\py38\lib\site-packages (from cython-bbox==0.1.3) (1.24.4)
Building wheels for collected packages: cython-bbox
  Building wheel for cython-bbox (setup.py) ... done
  Created wheel for cython-bbox: filename=cython_bbox-0.1.3-cp38-cp38-win_amd64.whl size=29288 sha256=ecd828eb5c0cdba3e3b4931d9acb1719cd0869ba675321ae59eba43b85f1368c
  Stored in directory: c:\users\xjsd\appdata\local\pip\cache\wheels\a2\15\10\68bac8f727391b0efe795d418657f56c18299c9404c06ad0ae
Successfully built cython-bbox
Installing collected packages: cython-bbox
Successfully installed cython-bbox-0.1.3
```


![在这里插入图片描述](/static/img/images/03934.png)

numpy装1.23版本，重启pycharm。

Python，如何安装cython_bbox

Linux可以：





```shell
pip install cpython
pip install git+git://github.com/gatagat/lap.git
```

windows可以：

下载`https://github.com/gatagat/lap` 后解压，

安装`pip install cpython`

安装VS2019企业版：

```shell
key
BF8Y8-GN2QH-T84XB-QVY3B-RC4DF
```

执行安装：

```
(py38) C:\Users\xjsd>cd C:\Users\xjsd\Downloads\lap-master\lap-master
(py38) C:\Users\xjsd\Downloads\lap-master\lap-master>python setup.py install
```

![在这里插入图片描述](/static/img/images/03935.png)

也可以：

```
pip install . # 安装当前路径的包
```

完成后：

![在这里插入图片描述](/static/img/images/03936.png)

又发现新版不好用，下个`https://github.com/gatagat/lap/tree/v0.4.0` 老版本的。

Python，如何安装lap，pip安装lap出现问题

CUDA可用，共有 1 个GPU设备可用。





当前使用的GPU设备索引：0

当前使用的GPU设备名称：NVIDIA T1000

GPU显存总量：4.00 GB

已使用的GPU显存：0.00 GB

剩余GPU显存：4.00 GB

PyTorch版本：1.10.1+cu102

```python
import torch
# 检查CUDA是否可用
cuda_available = torch.cuda.is_available()
if cuda_available:
    # 获取GPU设备数量
    num_gpu = torch.cuda.device_count()
    # 获取当前使用的GPU索引
    current_gpu_index = torch.cuda.current_device()
    # 获取当前GPU的名称
    current_gpu_name = torch.cuda.get_device_name(current_gpu_index)
    # 获取GPU显存的总量和已使用量
    total_memory = torch.cuda.get_device_properties(current_gpu_index).total_memory / (1024 ** 3)  # 显存总量(GB)
    used_memory = torch.cuda.memory_allocated(current_gpu_index) / (1024 ** 3)  # 已使用显存(GB)
    free_memory = total_memory - used_memory  # 剩余显存(GB)
    print(f"CUDA可用，共有 {num_gpu} 个GPU设备可用。")
    print(f"当前使用的GPU设备索引：{current_gpu_index}")
    print(f"当前使用的GPU设备名称：{current_gpu_name}")
    print(f"GPU显存总量：{total_memory:.2f} GB")
    print(f"已使用的GPU显存：{used_memory:.2f} GB")
    print(f"剩余GPU显存：{free_memory:.2f} GB")
else:
    print("CUDA不可用。")
# 检查PyTorch版本
print(f"PyTorch版本：{torch.__version__}")
import torch
print(f"CUDA版本：{torch.version.cuda}")
```


windows先装显卡驱动，再装CUDA10.2，最后装了pytorch。

pip install torch==1.10.1+cu102 torchvision==0.13.1+cu102 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu102


![在这里插入图片描述](/static/img/images/03938.png)



```python
import torch
import os
import subprocess

def check_gpu_availability():
    print("==== Checking GPU Availability ====")
    if torch.cuda.is_available():
        print(f"GPU is available. Number of GPUs: {torch.cuda.device_count()}")
        for i in range(torch.cuda.device_count()):
            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
    else:
        print("No GPU available. Please check your hardware or drivers.")
        return False
    return True

def check_cuda_and_cudnn():
    print("\n==== Checking CUDA and cuDNN Availability ====")
    if torch.cuda.is_available():
        print(f"CUDA version: {torch.version.cuda}")
        print(f"cuDNN version: {torch.backends.cudnn.version()}")
        print(f"cuDNN enabled: {torch.backends.cudnn.enabled}")
    else:
        print("CUDA or cuDNN not available. Please check the installation.")

def test_gpu_computation():
    print("\n==== Testing Simple Computation on GPU ====")
    try:
        
        device = torch.device("cuda:2")
        a = torch.randn(50, 50, device=device)
        b = torch.randn(50, 50, device=device)
        c = torch.matmul(a, b)
        print("Matrix multiplication on GPU successful.")
    except Exception as e:
        print(f"Error during computation on GPU: {e}")

def check_gpu_memory():
    print("\n==== Checking GPU Memory Usage ====")
    try:
        gpu_memory = torch.cuda.memory_summary()
        print(gpu_memory)
    except Exception as e:
        print(f"Error retrieving GPU memory info: {e}")

def run_nvidia_smi():
    print("\n==== Running nvidia-smi Command ====")
    try:
        # Run the nvidia-smi command and capture the output
        result = subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
        if result.returncode == 0:
            print(result.stdout)
        else:
            print(f"Error running nvidia-smi: {result.stderr}")
    except Exception as e:
        print(f"Error running nvidia-smi command: {e}")

def main():
    print("==== Starting GPU Health Check ====")
    
    # Step 1: Check GPU availability
    if not check_gpu_availability():
        return
    
    # Step 2: Check CUDA and cuDNN versions
    check_cuda_and_cudnn()
    
    # Step 3: Test GPU computation
    test_gpu_computation()
    
    # Step 4: Check GPU memory usage
    check_gpu_memory()
    
    # Step 5: Run nvidia-smi to check GPU status
    run_nvidia_smi()
    
    print("\n==== GPU Health Check Completed ====")

if __name__ == "__main__":
    main()

```

【Python】pytorch，CUDA是否可用，查看显卡显存剩余容量

# conda





```c
conda config --set ssl_verify no
conda config --set proxy_servers.http <VPN 服务器地址>:<VPN 服务器端口>
conda config --set proxy_servers.https <VPN 服务器地址>:<VPN 服务器端口>
```


```c
conda install <package_name>
```


# pip

```c
pip --proxy <VPN 服务器地址>:<VPN 服务器端口> install requests
```

example.


```c
 pip install --proxy http://127.0.0.1:10809 -r req.txt
```

给conda、pip提提速，给他一个vpn，还世界清静

# 环境





```shell
pip install jupyter 
```

# 使用

在你想访问的目录下执行：


```c
jupyter notebook --ip=0.0.0.0
```

jupyter 给出提示：

```c
[I 2023-07-28 14:32:43.589 ServerApp] Package notebook took 0.0000s to import
[I 2023-07-28 14:32:43.597 ServerApp] Package jupyter_lsp took 0.0081s to import
[W 2023-07-28 14:32:43.597 ServerApp] A `_jupyter_server_extension_points` function was not found in jupyter_lsp. Instead, a `_jupyter_server_extension_paths` function was found and will be used for now. This function name will be deprecated in future releases of Jupyter Server.
[I 2023-07-28 14:32:43.601 ServerApp] Package jupyter_server_terminals took 0.0034s to import
[I 2023-07-28 14:32:43.601 ServerApp] Package jupyterlab took 0.0000s to import
[I 2023-07-28 14:32:43.639 ServerApp] Package notebook_shim took 0.0000s to import
[W 2023-07-28 14:32:43.640 ServerApp] A `_jupyter_server_extension_points` function was not found in notebook_shim. Instead, a `_jupyter_server_extension_paths` function was found and will be used for now. This function name will be deprecated in future releases of Jupyter Server.
[I 2023-07-28 14:32:43.640 ServerApp] jupyter_lsp | extension was successfully linked.
[I 2023-07-28 14:32:43.643 ServerApp] jupyter_server_terminals | extension was successfully linked.
[I 2023-07-28 14:32:43.647 ServerApp] jupyterlab | extension was successfully linked.
[I 2023-07-28 14:32:43.651 ServerApp] notebook | extension was successfully linked.
[I 2023-07-28 14:32:43.654 ServerApp] Writing Jupyter server cookie secret to /home/xd/.local/share/jupyter/runtime/jupyter_cookie_secret
[I 2023-07-28 14:32:43.827 ServerApp] notebook_shim | extension was successfully linked.
[I 2023-07-28 14:32:43.930 ServerApp] notebook_shim | extension was successfully loaded.
[I 2023-07-28 14:32:43.932 ServerApp] jupyter_lsp | extension was successfully loaded.
[I 2023-07-28 14:32:43.933 ServerApp] jupyter_server_terminals | extension was successfully loaded.
[I 2023-07-28 14:32:43.934 LabApp] JupyterLab extension loaded from /ssd/xd/miniconda3/envs/py38_diffusers_1/lib/python3.8/site-packages/jupyterlab
[I 2023-07-28 14:32:43.934 LabApp] JupyterLab application directory is /ssd/xd/miniconda3/envs/py38_diffusers_1/share/jupyter/lab
[I 2023-07-28 14:32:43.935 LabApp] Extension Manager is 'pypi'.
[I 2023-07-28 14:32:43.937 ServerApp] jupyterlab | extension was successfully loaded.
[I 2023-07-28 14:32:43.939 ServerApp] notebook | extension was successfully loaded.
[I 2023-07-28 14:32:43.939 ServerApp] Serving notebooks from local directory: /ssd/xd/workplace
[I 2023-07-28 14:32:43.939 ServerApp] Jupyter Server 2.7.0 is running at:
[I 2023-07-28 14:32:43.939 ServerApp] http://gpu20:8888/tree?token=490e738359e9e3a73247822c61620bacdfcae0038a9a721e
[I 2023-07-28 14:32:43.939 ServerApp]     http://127.0.0.1:8888/tree?token=490e738359e9e3a73247822c61620bacdfcae0038a9a721e
[I 2023-07-28 14:32:43.939 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 2023-07-28 14:32:43.948 ServerApp] No web browser found: Error('could not locate runnable browser').
```


记住这提示里的token：

490e738359e9e3a73247822c61620bacdfcae0038a9a721e

# 访问

windows电脑浏览器访问：

http://101.20.31.20:8888


默认端口8888，前面的ip需要填写你Linux服务器的IP。

【Python】jupyter Linux服务器使用

基础环境：





```c
sudo apt-get install ffmpeg
```


# m4a


要将M4A文件转换为MP3文件，你可以使用Python中的第三方库`pydub`。`pydub`使得音频处理变得非常简单。在开始之前，请确保你已经安装了`pydub`库，如果没有，可以通过以下命令安装：

```bash
pip install pydub
```

接下来，你可以使用以下代码将M4A文件转换为MP3文件：

```python
from pydub import AudioSegment
def m4a_to_mp3(m4a_file, mp3_file):
    # Load the M4A file
    audio = AudioSegment.from_file(m4a_file, format="m4a")
    # Export the audio as MP3
    audio.export(mp3_file, format="mp3")
if __name__ == "__main__":
    input_m4a_file = "input_file.m4a"   # Replace with the path to your M4A file
    output_mp3_file = "output_file.mp3" # Replace with the desired output MP3 file path
    m4a_to_mp3(input_m4a_file, output_mp3_file)
```

将上述代码中的`input_m4a_file`替换为你要转换的M4A文件路径，`output_mp3_file`替换为你希望输出的MP3文件路径。然后运行代码，它将加载M4A文件并将其转换为MP3格式。请注意，转换过程可能需要一些时间，具体取决于文件大小和你的计算机性能。

提醒：在进行音频格式的转换时，请遵守相关版权法规和使用规定，确保你有权处理和使用这些文件。




# aac

要将AAC文件转换为MP3文件，你可以使用Python中的`pydub`库，它提供了一个简单的方法来进行音频格式转换。在开始之前，请确保已经安装了`pydub`库，如果没有，可以通过以下命令安装：

```bash
pip install pydub
```

接下来，你可以使用以下代码将AAC文件转换为MP3文件：

```python
from pydub import AudioSegment
def aac_to_mp3(aac_file, mp3_file):
    # Load the AAC file
    audio = AudioSegment.from_file(aac_file, format="aac")
    # Export the audio as MP3
    audio.export(mp3_file, format="mp3")
if __name__ == "__main__":
    input_aac_file = "input_file.aac"   # Replace with the path to your AAC file
    output_mp3_file = "output_file.mp3" # Replace with the desired output MP3 file path
    aac_to_mp3(input_aac_file, output_mp3_file)
```

将上述代码中的`input_aac_file`替换为你要转换的AAC文件路径，`output_mp3_file`替换为你希望输出的MP3文件路径。然后运行代码，它将加载AAC文件并将其转换为MP3格式。请注意，转换过程可能需要一些时间，具体取决于文件大小和你的计算机性能。

同样地，在进行音频格式的转换时，请确保你有权处理和使用这些文件，遵守相关版权法规和使用规定。

【Python】将M4A\AAC录音文件转换为MP3文件

Python代码：




```powershell
import os
def split_file(input_file, output_dir, chunk_size):
    with open(input_file, 'rb') as f:
        index = 0
        while True:
            chunk = f.read(chunk_size)
            if not chunk:
                break
            output_file = os.path.join(output_dir, f'chunk_{index}.dat')
            with open(output_file, 'wb') as out:
                out.write(chunk)
            index += 1
def merge_files(input_dir, output_file):
    files = os.listdir(input_dir)
    files.sort()
    with open(output_file, 'wb') as out:
        for file_name in files:
            file_path = os.path.join(input_dir, file_name)
            with open(file_path, 'rb') as f:
                chunk = f.read()
                out.write(chunk)
# 切分大文件
split_file('big_file.dat', 'output_dir', 10 * 1024 * 1024)  # 每个小文件大小为10GB
# 合并小文件
merge_files('output_dir', 'merged_file.dat')
```

【Python】大文件切分为小文件，小文件组合为大文件

# 概括





注意，在哪个平台运行打包，就会对应生成那个平台的可执行文件。比如我在windows中执行，我将会得到exe执行包。

使用PyInstaller打包Python代码成为可执行的exe文件非常简单。以下是基本的步骤：

**1、确保你已经安装了PyInstaller。如果没有安装，可以通过以下命令来安装：**

```powershell
pip install pyinstaller
```


**2、打开终端或命令提示符，并导航到你的Python代码所在的目录。**

**3、在终端或命令提示符中运行以下命令：**

```powershell
pyinstaller -F your_script.py
```

其中，your_script.py 是你要打包的Python脚本的文件名。

**4、PyInstaller将会自动分析你的脚本，并创建一个与你的脚本同名的文件夹。**

**5、在生成的文件夹中，你可能会看到一些额外的文件，例如.spec文件和一个名为dist的文件夹。**

**6、最终的可执行文件可以在dist文件夹中找到，文件名通常与你的脚本的文件名相同。**

现在，你就可以将生成的可执行文件(.exe)拷贝到其他机器上，并在不安装Python解释器的情况下运行你的代码了。


常用指令：

```powershell
Pyinstaller -F setup.py # 打包exe
Pyinstaller -F -w setup.py # 不带控制台的打包
Pyinstaller -F -i xx.ico setup.py # 打包指定exe图标打包
```

# 混合打包

参考：

https://blog.csdn.net/tangfreeze/article/details/112240342

https://pyinstaller.org/en/stable/spec-files.html#adding-files-to-the-bundle

我想将一些资源文件也打包进exe如何办？

运行：

```powershell
pyinstaller -F -w -i 2.ico  --add-data  ".\1.ico;."  setup.py
```

-i 图标路径

–icon=图标路径

-F 打包成一个exe文件

-w 使用窗口，无控制台

-c 使用控制台，无窗口

-D 创建一个目录，里面包含exe以及其他一些依赖性文件


# 重要！

完成打包后，setup.exe里面如果依赖了外部文件，应该注意python找文件的写法：

![在这里插入图片描述](/static/img/images/04384.png)

比如：

```python
import sys
import tkinter as tk
from os import path
from tkinter import ttk
import serial
import threading
# 先获取当前运行时临时目录路径
if getattr(sys, 'frozen', None):
    basedir = sys._MEIPASS
else:
    basedir = path.dirname(__file__)
ico_file = path.join(basedir, '1.ico')
```