I have a binary file with "myfile.gu1" format. It has several rows and columns that from inside it should look like this:
I would like to convert this file to a csv file or a a dataframe so I can then plot the data inside it. The data in the binary file should have around 70 headers and the first column is just the system time. Codes that I tried from Stackoverflow, do convert the data but at the end I get files with many characters that are not defined. This table that I provided, is converted by the measurement PC and that is very slow and cannot be automatized. any ideas for solution is highly appreciated!
I am sure that my code is far from working:
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) 1 Answer
I'm not sure if this is what you need but it give me an output that is similar to the one you give us as example.
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") Output:
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] I hope it helps
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