How to Convert TMESH Data to Text Files in MCNP6 Using Python"

  • #1
rai915
3
2
I would like to analyse mdata of tmesh in MCNP6 by python, but I am struggling how to convert mdata (unformatted binary) to any text file in python.
I used GRIDCONV but it's not suitable for automations.

I also used scipy.io.Fortrunfile but could not convert the file because I was not sure fortrun setting for output, like endians.
 
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  • #2
Welcome to PF!

Are you also generating a mctal file and does this not have what you need as text when you do a tmesh?

If the data is right https://github.com/ipostuma/MCNPtools might help to plot what you want.
 
  • #3
Alex A said:
Welcome to PF!

Are you also generating a mctal file and does this not have what you need as text when you do a tmesh?

If the data is right https://github.com/ipostuma/MCNPtools might help to plot what you want.
Thank you for the reply, but I want to have MDATA generated by tmesh option, not mctal.
MDATA is the tmesh tally data, with unformatted binary.
 
  • #4
I'm assuming the reason you don't want to use gridconv is because it's interactive. What OS are you using?

For linux if you made a program that printed
Code:
y
mdatatext
and ran it with program.py | gridconv
That would take a binary format file called mdata and convert it to a text file called mdatatext
That is three enters, y(enter), mdatatext(enter), and two enters. This is for an older version but if it's changed I imagine it won't be much.

Patching the source would also work, if you don't have the source patching the binary will also work.
 
  • #5
Thank you Alex, I solved it to control interactive GUIDCONV by pexpect:)
 
  • Like
Likes zhj2024 and Alex A

Related to How to Convert TMESH Data to Text Files in MCNP6 Using Python"

1. What is TMESH data in MCNP6?

TMESH data in MCNP6 refers to a tally mesh, which is a spatial grid used to record various types of simulation data, such as flux, dose, or energy deposition, across a defined geometry in a Monte Carlo N-Particle (MCNP) simulation. It allows for detailed spatial analysis of simulation results.

2. How do I read TMESH data from an MCNP6 output file using Python?

To read TMESH data from an MCNP6 output file using Python, you can use libraries such as NumPy and Pandas for data manipulation, and regular expressions (re module) to parse the text. Typically, you would open the output file, search for the TMESH data section, and then extract the relevant numerical data into a structured format like a DataFrame.

3. What Python libraries are useful for converting TMESH data to text files?

Useful Python libraries for converting TMESH data to text files include NumPy for numerical operations, Pandas for data manipulation and storage, and the built-in csv module for writing data to text files. Additionally, the re module can be helpful for parsing text data from the MCNP6 output files.

4. How can I ensure the accuracy of the converted TMESH data?

To ensure the accuracy of the converted TMESH data, you should validate the parsed data against known values or a subset of the data manually. Additionally, you can write unit tests to check the correctness of your parsing and conversion functions. Cross-referencing with the original MCNP6 output file helps to verify that the data has been accurately extracted and converted.

5. Can you provide a basic Python script example to convert TMESH data to a text file?

Sure, here is a basic example:

import reimport pandas as pddef read_tmesh_data(file_path):    with open(file_path, 'r') as file:        data = file.read()    tmesh_section = re.search(r'TMESH.*?END TMESH', data, re.DOTALL)    if not tmesh_section:        raise ValueError("TMESH section not found")    tmesh_data = tmesh_section.group(0)    lines = tmesh_data.split('\n')        # Assuming the data starts after a specific line    start_index = next(i for i, line in enumerate(lines) if 'Data Start' in line) + 1    data_lines = lines[start_index:]    # Convert to DataFrame    df = pd.DataFrame([line.split() for line in data_lines if line], columns=['x', 'y', 'z', 'value'])    df = df.apply(pd.to_numeric)
		

	









	
			

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