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Mileta Avramovic
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How to Use Voyager 1 and Voyager 2 Data for Projects (CDF)

·407 words·2 mins
Mileta Avramovic
Author
Mileta Avramovic
Information Technologies student at Faculty of Sciences University of Novi Sad.
Table of Contents

How to Use Voyager 1 and Voyager 2 Data for Project (CDF)
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Voyager 1 and 2 Data Overview
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Familiarizing yourself with the CDF data format is beneficial. Learn more about it at CDF (Common Data Format). Additionally, understanding NASA’s directory structure and data management practices will help you work effectively with mission data.

Accessing Voyager Data
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Voyager 1 and 2 data are available through NASA’s PDS PPI node:

  • Visit the NASA PDS PPI Portal to search and download CDF files related to Voyager and other missions.

Processing Voyager Data: From CDF to CSV
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To analyze Voyager data more efficiently, converting CDF files to CSV is recommended.

Tools for CDF File Processing
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  • Autoplot: Open and inspect CDF files. Autoplot website.
  • Python Script: Use the following script to convert CDF files to CSV. The script requires the spacepy library, which is specifically designed for handling CDF files.

Python Script for CDF to CSV Conversion
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Clone the script from GitHub or use the code below:

import os
import pandas as pd
from spacepy import pycdf

def process_cdf_file(cdf_file_path, output_csv_path):
    try:
        print(f"Processing file: {cdf_file_path}")
        # Open the CDF file
        with pycdf.CDF(cdf_file_path) as cdf:
            # Extract 'epoch' and 'electric_field' data
            epoch_data = cdf['epoch'][:]
            electric_field_data = cdf['electric_field'][:]

            # Create a DataFrame with 'epoch' as index and 'electric_field' as columns
            df = pd.DataFrame(electric_field_data, index=epoch_data)

            # Rename columns
            columns = [str(freq) + ' Hz' for freq in cdf['frequency'][:]]
            df.columns = columns

            # Export the DataFrame to a CSV file
            df.to_csv(output_csv_path)
        print(f"Saved CSV to: {output_csv_path}")
    except Exception as e:
        print(f"Failed to process {cdf_file_path}: {e}")

def process_directory(parent_dir, output_csv_dir):
    cdf_count = 0
    csv_count = 0

    # Walk through all directories and subdirectories
    for root, _, files in os.walk(parent_dir):
        for file in files:
            if file.endswith('.cdf'):
                cdf_count += 1
                # Construct full file path
                cdf_file_path = os.path.join(root, file)

                # Create the corresponding directory structure in the output directory
                relative_path = os.path.relpath(root, parent_dir)
                output_dir_path = os.path.join(output_csv_dir, relative_path)
                os.makedirs(output_dir_path, exist_ok=True)

                # Define the output CSV file path
                output_csv_path = os.path.join(output_dir_path, os.path.splitext(file)[0] + '.csv')

                # Process the CDF file and save to CSV
                process_cdf_file(cdf_file_path, output_csv_path)
                if os.path.exists(output_csv_path):
                    csv_count += 1

    print(f"Total CDF files found: {cdf_count}")
    print(f"Total CSV files created: {csv_count}")

if __name__ == "__main__":
    # Specify the parent directory containing CDF files
    parent_directory = ''

    # Specify the output directory for CSV files
    output_csv_directory = ''

    print(f"Processing directory: {parent_directory}")
    print(f"Output will be saved to: {output_csv_directory}")

    # Process all CDF files in the parent directory
    process_directory(parent_directory, output_csv_directory)

    print("Processing complete.")