python 种f.write(u+b'\n')

在Python中，f.write（）函数用于向文件中写入数据。如果要在文件中写入新的一行，可以使用'\n'字符作为分隔符。但是，在Python 3中，所有的字符串默认都是Unicode编码，因此写入文件时需要将字符串转换为bytes类型。

这时，可以使用前缀"b"或者bytes（）函数将字符串转换为bytes类型。例如，f.write（b'hello world\n'）就是将"hello world"字符串转换为bytes类型，并在末尾添加一个新行符号"\n"来创建新的一行。

由于Python 3的默认编码是UTF-8，因此可以使用utf-8编码来转换字符串，例如f.write（"你好，世界".encode（"utf-8"））。

总之，python种f.write(u b'\n')是将Unicode编码的"\n"字符串转换为bytes类型，并使用f.write()函数将其写入文件中，用于创建新的一行。

相关问题

import os import sys import numpy as np def creat_pcd(input_path, output_path): #Lodaing txt Full_Data = np.loadtxt(input_path) #Creating pcd if os.path.exists(output_path): os.remove(output_path) Output_Data = open(output_path, 'a') Output_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1') string = '\nWIDTH ' + str(Full_Data.shape[0]) Output_Data.write(string) Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0') string = '\nPOINTS ' + str(Full_Data.shape[0]) Output_Data.write(string) Output_Data.write('\nDATA ascii') for j in range(Full_Data.shape[0]): R=Full_Data[j,1] G=Full_Data[j,1] B=Full_Data[j,2] value = (int(R) << 16 | int(G) << 8 | int(B)) string = ('\n' + str(Full_Data[j,0]) + ' ' + str(Full_Data[j, 1]) + ' ' +str(Full_Data[j, 2]) + ' ' + str(value)) Output_Data.write(string) Output_Data.close() print('--------------Completed--------------') a = input("请输入TXT文件路径：")#文件路径中斜杆使用"/"，比如：D:/pcl/points.txt b = input("请输入PCD文件保存路径：")#比如：D:/pcl/points.pcd creat_pcd(a, b)对这些代码优化

以下是对代码的优化建议：

1. 引入模块应当分行，不要写在同一行。

2. 应使用 with 语句打开文件，可以自动关闭文件，避免忘记关闭文件而导致资源泄露。

3. 在写入文件时应当使用字符串格式化来避免繁琐的字符串拼接操作，提高代码可读性。

4. 变量名应该具有具体的描述性，便于代码阅读和理解。

5. 字段名称和数据类型定义可以提取为常量或变量，便于修改和维护。

6. 可以使用 argparse 模块来处理命令行参数，更加方便用户使用。

下面是优化后的代码：

import os
import sys
import argparse
import numpy as np

PCD_FIELDS = ['x', 'y', 'z', 'rgba']
PCD_SIZE = [4, 4, 4, 4]
PCD_TYPE = ['F', 'F', 'F', 'U']
PCD_COUNT = [1, 1, 1, 1]
PCD_VERSION = '0.7'


def create_pcd(input_path, output_path):
    # Loading txt
    full_data = np.loadtxt(input_path)

    # Creating pcd
    if os.path.exists(output_path):
        os.remove(output_path)

    with open(output_path, 'a') as output_data:
        output_data.write(f"# .PCD v{PCD_VERSION} - Point Cloud Data file format\n")
        output_data.write("VERSION {}\n".format(PCD_VERSION))
        output_data.write("FIELDS {}\n".format(" ".join(PCD_FIELDS)))
        output_data.write("SIZE {}\n".format(" ".join(map(str, PCD_SIZE))))
        output_data.write("TYPE {}\n".format(" ".join(PCD_TYPE)))
        output_data.write("COUNT {}\n".format(" ".join(map(str, PCD_COUNT))))

        output_data.write("WIDTH {}\n".format(full_data.shape[0]))
        output_data.write("HEIGHT 1\n")
        output_data.write("VIEWPOINT 0 0 0 1 0 0 0\n")
        output_data.write("POINTS {}\n".format(full_data.shape[0]))
        output_data.write("DATA ascii\n")

        for j in range(full_data.shape[0]):
            r = full_data[j, 1]
            g = full_data[j, 1]
            b = full_data[j, 2]
            value = (int(r) << 16 | int(g) << 8 | int(b))
            output_data.write("{:.6f} {:.6f} {:.6f} {:d}\n".format(full_data[j, 0], full_data[j, 1], full_data[j, 2], value))

    print('--------------Completed--------------')


def parse_args():
    parser = argparse.ArgumentParser(description='Convert txt file to pcd file.')
    parser.add_argument('input_path', type=str, help='input txt file path')
    parser.add_argument('output_path', type=str, help='output pcd file path')
    return parser.parse_args()


if __name__ == '__main__':
    args = parse_args()
    create_pcd(args.input_path, args.output_path)

使用 argparse 模块可以从命令行方便地输入文件路径，例如：

python create_pcd.py D:/pcl/points.txt D:/pcl/points.pcd

修改下面代码，另画一张可视化图展示出t_sne里面的数据每15行数据个用一种颜色画出。 import pandas as pd from sklearn import cluster from sklearn import metrics import matplotlib.pyplot as plt from sklearn.manifold import TSNE from sklearn.decomposition import PCA def k_means(data_set, output_file, png_file, t_labels, score_file, set_name): model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++") model.fit(data_set) # print(list(model.labels_)) p_labels = list(model.labels_) r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1) r.columns = list(data_set.columns) + [u'聚类类别'] print(r) # r.to_excel(output_file) with open(score_file, "a") as sf: sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n") sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n") '''pca = PCA(n_components=2) pca.fit(data_set) pca_result = pca.transform(data_set) t_sne = pd.DataFrame(pca_result, index=data_set.index)''' t_sne = TSNE() t_sne.fit(data_set) t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False dd = t_sne[r[u'聚类类别'] == 0] plt.plot(dd[0], dd[1], 'r.') dd = t_sne[r[u'聚类类别'] == 1] plt.plot(dd[0], dd[1], 'go') dd = t_sne[r[u'聚类类别'] == 2] plt.plot(dd[0], dd[1], 'b*') dd = t_sne[r[u'聚类类别'] == 3] plt.plot(dd[0], dd[1], 'o') dd = t_sne[r[u'聚类类别'] == 4] plt.plot(dd[0], dd[1], 'm.') dd = t_sne[r[u'聚类类别'] == 5] plt.plot(dd[0], dd[1], 'co') dd = t_sne[r[u'聚类类别'] == 6] plt.plot(dd[0], dd[1], 'y*') plt.savefig(png_file) plt.clf() '''plt.scatter(data_set.iloc[:, 0], data_set.iloc[:, 1], c=model.labels_) plt.savefig(png_file) plt.clf()''' frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv") tLabel = [] for family in frog_data['name']: if family == "A": tLabel.append(0) elif family == "B": tLabel.append(1) elif family == "C": tLabel.append(2) elif family == "D": tLabel.append(3) elif family == "E": tLabel.append(4) elif family == "F": tLabel.append(5) elif family == "G": tLabel.append(6) scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt" first_set = frog_data.iloc[:, 1:1327] k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", tLabel, scoreFile, "Set_1")

修改后的代码如下所示：

import pandas as pd
from sklearn import cluster
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE

from sklearn.decomposition import PCA

def k_means(data_set, output_file, png_file, t_labels, score_file, set_name):
    model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++")
    model.fit(data_set)
    p_labels = list(model.labels_)
    r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1)
    r.columns = list(data_set.columns) + [u'聚类类别']
    print(r)
    with open(score_file, "a") as sf:
        sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n")
        sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n")

    t_sne = TSNE()
    t_sne.fit(data_set)
    t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index)
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    
    colors = ['r', 'g', 'b', 'o', 'm', 'c', 'y']
    
    for i in range(0, len(t_sne), 15):
        dd = t_sne[r[u'聚类类别'] == i]
        plt.plot(dd[0], dd[1], colors[i % len(colors)]+'.')
    
    plt.savefig(png_file)
    plt.clf()

frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv")
tLabel = []
for family in frog_data['name']:
    if family == "A":
        tLabel.append(0)
    elif family == "B":
        tLabel.append(1)
    elif family == "C":
        tLabel.append(2)
    elif family == "D":
        tLabel.append(3)
    elif family == "E":
        tLabel.append(4)
    elif family == "F":
        tLabel.append(5)
    elif family == "G":
        tLabel.append(6)

scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt"
first_set = frog_data.iloc[:, 1:1327]
k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", tLabel, scoreFile, "Set_1")

这段代码对原始代码进行了修改，使用了循环来绘制 t_sne 中每15行数据的散点图，并且使用不同的颜色来表示每个类别。修改后的代码会生成一个名为 "kMeansSet_2.png" 的可视化图。请确保已经安装了相关的依赖库，并将相关路径替换为正确的文件路径。