是否可以使用 Python csv reader 读取由 C++ std::setw 生成的文件?
Is it possible to read files generated with C++ std::setw using Python csv reader?
我有一个使用 C++ 生成的数据文件 std::setw
例如
file << std::scientific << std::setprecision(data_precision);
for (double data : a_data)
{
file << std::setw(data_width) << data;
}
file << "\n";
是否可以使用 python csv.reader 或类似的方式读取数据?我尝试了以下方法:
with data as csvfile:
fieldreader = csv.reader(csvfile)
next(fieldreader)
for row in fieldreader:
values.append(float(row[0]))
输出整个第一行,表示整行存储为一个条目。我也尝试了一些不同的分隔符,例如\t
没有帮助。
示例输出如下:
# z phi phi1 Massless
-16.0000000 0.0000000 9.9901854997e-01 1.0910677716e-19
-16.0000000 0.0245437 9.9871759471e-01 1.6545142956e-05
-16.0000000 0.0490874 9.9781493216e-01 3.3051500271e-05
-16.0000000 0.0736311 9.9631097893e-01 4.9477653557e-05
-16.0000000 0.0981748 9.9420658732e-01 6.5784269579e-05
csvfile
argument 到 csv.reader
初始值设定项 "can be any object which supports the iterator protocol and returns a string each time its next()
method is called"。
这意味着您可以通过定义如下所示的生成器函数来读取文件,以预处理文件的行,使它们可以被 csv.reader
:
import csv
def preprocess(file):
for line in file:
yield ','.join(line.split())
values = []
with open('cppfile.txt') as file:
fieldreader = csv.reader(preprocess(file))
next(fieldreader)
for row in fieldreader:
print(f'row={row}')
values.append(float(row[0]))
print()
print(values)
输出:
row=['-16.0000000', '0.0000000', '9.9901854997e-01', '1.0910677716e-19']
row=['-16.0000000', '0.0245437', '9.9871759471e-01', '1.6545142956e-05']
row=['-16.0000000', '0.0490874', '9.9781493216e-01', '3.3051500271e-05']
row=['-16.0000000', '0.0736311', '9.9631097893e-01', '4.9477653557e-05']
row=['-16.0000000', '0.0981748', '9.9420658732e-01', '6.5784269579e-05']
[-16.0, -16.0, -16.0, -16.0, -16.0]
我会选择pandas,一个出色的第三方库,提供高性能、易用的数据结构和数据分析工具,来解析您提到的生成文件:
example.txt
# z phi phi1 Massless
-16.0000000 0.0000000 9.9901854997e-01 1.0910677716e-19
-16.0000000 0.0245437 9.9871759471e-01 1.6545142956e-05
-16.0000000 0.0490874 9.9781493216e-01 3.3051500271e-05
-16.0000000 0.0736311 9.9631097893e-01 4.9477653557e-05
-16.0000000 0.0981748 9.9420658732e-01 6.5784269579e-05
test.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
if __name__ == "__main__":
df = pd.read_csv("test.txt", sep=r'\s+', skiprows=1, names=["z", "phi", "phi1", "Massless",])
print(df)
在运行命令之后如下:
python test.py
我得到以下结果:
z phi phi1 Massless
0 -16.0 0.000000 0.999019 1.091068e-19
1 -16.0 0.024544 0.998718 1.654514e-05
2 -16.0 0.049087 0.997815 3.305150e-05
3 -16.0 0.073631 0.996311 4.947765e-05
4 -16.0 0.098175 0.994207 6.578427e-05
我有一个使用 C++ 生成的数据文件 std::setw
例如
file << std::scientific << std::setprecision(data_precision);
for (double data : a_data)
{
file << std::setw(data_width) << data;
}
file << "\n";
是否可以使用 python csv.reader 或类似的方式读取数据?我尝试了以下方法:
with data as csvfile:
fieldreader = csv.reader(csvfile)
next(fieldreader)
for row in fieldreader:
values.append(float(row[0]))
输出整个第一行,表示整行存储为一个条目。我也尝试了一些不同的分隔符,例如\t
没有帮助。
示例输出如下:
# z phi phi1 Massless
-16.0000000 0.0000000 9.9901854997e-01 1.0910677716e-19
-16.0000000 0.0245437 9.9871759471e-01 1.6545142956e-05
-16.0000000 0.0490874 9.9781493216e-01 3.3051500271e-05
-16.0000000 0.0736311 9.9631097893e-01 4.9477653557e-05
-16.0000000 0.0981748 9.9420658732e-01 6.5784269579e-05
csvfile
argument 到 csv.reader
初始值设定项 "can be any object which supports the iterator protocol and returns a string each time its next()
method is called"。
这意味着您可以通过定义如下所示的生成器函数来读取文件,以预处理文件的行,使它们可以被 csv.reader
:
import csv
def preprocess(file):
for line in file:
yield ','.join(line.split())
values = []
with open('cppfile.txt') as file:
fieldreader = csv.reader(preprocess(file))
next(fieldreader)
for row in fieldreader:
print(f'row={row}')
values.append(float(row[0]))
print()
print(values)
输出:
row=['-16.0000000', '0.0000000', '9.9901854997e-01', '1.0910677716e-19']
row=['-16.0000000', '0.0245437', '9.9871759471e-01', '1.6545142956e-05']
row=['-16.0000000', '0.0490874', '9.9781493216e-01', '3.3051500271e-05']
row=['-16.0000000', '0.0736311', '9.9631097893e-01', '4.9477653557e-05']
row=['-16.0000000', '0.0981748', '9.9420658732e-01', '6.5784269579e-05']
[-16.0, -16.0, -16.0, -16.0, -16.0]
我会选择pandas,一个出色的第三方库,提供高性能、易用的数据结构和数据分析工具,来解析您提到的生成文件:
example.txt
# z phi phi1 Massless
-16.0000000 0.0000000 9.9901854997e-01 1.0910677716e-19
-16.0000000 0.0245437 9.9871759471e-01 1.6545142956e-05
-16.0000000 0.0490874 9.9781493216e-01 3.3051500271e-05
-16.0000000 0.0736311 9.9631097893e-01 4.9477653557e-05
-16.0000000 0.0981748 9.9420658732e-01 6.5784269579e-05
test.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
if __name__ == "__main__":
df = pd.read_csv("test.txt", sep=r'\s+', skiprows=1, names=["z", "phi", "phi1", "Massless",])
print(df)
在运行命令之后如下:
python test.py
我得到以下结果:
z phi phi1 Massless
0 -16.0 0.000000 0.999019 1.091068e-19
1 -16.0 0.024544 0.998718 1.654514e-05
2 -16.0 0.049087 0.997815 3.305150e-05
3 -16.0 0.073631 0.996311 4.947765e-05
4 -16.0 0.098175 0.994207 6.578427e-05