将二进制文件 gu1 转换为 csv 和数据帧

Question

我有一个“myfile.gu1”格式的二进制文件。它有几行和几列，从里面看应该是这样的：

我想将此文件转换为 csv 文件或数据框，以便我可以绘制其中的数据。二进制文件中的数据应该有大约 70 个标题，第一列只是系统时间。我从 Stackoverflow 尝试的代码确实转换了数据，但最后我得到的文件包含许多未定义的字符。我提供的这张表是由测量 PC 转换的，速度很慢，无法自动化。非常感谢任何解决方案的想法！

我确定我的代码无法正常工作：

import os
import struct
import csv

# Get the current working directory
cwd = os.getcwd()

# Construct the full path to the binary file
filename = 'myfile.gu1'
filepath = os.path.join(cwd, 'Desktop', filename)

# Open the binary file in read binary mode
with open(filepath, 'rb') as file:
    # Read the binary data and convert it to a list of floats
    data = []
    while True:
        # Read 4 bytes of binary data as a float
        bytes = file.read(4)
        if not bytes:
            break
        float_value = struct.unpack('f', bytes)[0]
        data.append(float_value)
        
# Convert the list of floats to a list of lists with one float per row
rows = [[x] for x in data]

# Construct the full path to the csv file
csv_filename = os.path.splitext(filename)[0] + '.csv'
csv_filepath = os.path.join(cwd, 'Desktop', csv_filename)

# Write the list of lists to a csv file
with open(csv_filepath, 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(rows)

Answer 1

我不确定这是否是您需要的，但它给我的输出类似于您给我们举的例子。

import os
import struct
import pandas as pd

cwd = os.getcwd()

filename = 'Myfille.gu1'
# filepath = os.path.join(cwd, 'Desktop', filename)

with open(filename, 'rb') as file:
    rows = []
    data = []
    cont = 0
    while True:
        bytes = file.read(2)
        if cont == 111:
          rows.append(data)
          data = []
          cont = 0
        if not bytes:
          break
        try:
          bytes = bytes + b'\x00\x00'
          float_value = struct.unpack('I', bytes)[0]
          data.append(float_value)
          cont+=1
        except:
          print(bytes)
        
rows = pd.DataFrame(rows)
print(rows)

rows.to_csv("Myfille.csv")

输出：

b'\x02\x00\x00'
         0      1    2    3    4    5    6    7    8     9    ...    101  102  \
0      17216  39225  565  567  569  568  567  457  457   458  ...  14336    2   
1      17216  39230  566  566  568  567  569  456  459   458  ...  14336    2   
2      17216  40000  566  568  569  568  567  457  458   458  ...  14336    2   
3      17216  41000  567  569  570  568  571  454  456   457  ...  14336    2   
4      17216  42000  565  566  568  567  567  456  458   458  ...  14336    2   
...      ...    ...  ...  ...  ...  ...  ...  ...  ...   ...  ...    ...  ...   
43355  18496  57815  258  566  567  569  568  569  516  1024  ...      0    0   
43356  18496  57820  258  566  567  569  568  569  516  1024  ...      0    0   
43357  18496  57825  258  566  567  569  568  569  516  1024  ...      0    0   
43358  18496  57830  258  566  567  569  568  569  516  1024  ...      0    0   
43359  18496  57835  258  566  567  569  568  569  516  1024  ...      0    0   

       103  104  105    106  107    108   109  110  
0        0    0    0      0    0  57602  6568  527  
1        0    0    0      0    0  57602  6568  527  
2        0    0    0      0    0  58114  6568  527  
3        0    0    0      0    0  58114  6568  527  
4        0    0    0      0    0  58370  6568  527  
...    ...  ...  ...    ...  ...    ...   ...  ...  
43355    0    0    0  13824    0      0  5384  342  
43356    0    0    0  13824    0      0  5384  342  
43357    0    0    0  13824    0      0  5384  342  
43358    0    0    0  13824    0      0  5384  342  
43359    0    0    0  13824    0      0  5384  342  

[43360 rows x 111 columns]

希望对你有帮助