Metadata-Version: 2.1
Name: spc-export
Version: 1.0.1
Summary: SPC-Export is a tool designed to facilitate the conversion of SPC from Brama measurements into a format that is both human- and Python-readable. This streamlined conversion ensures easy access, readability, and visualization of measurement data for further analysis and interpretation.
Home-page: https://gitlab.tuwien.ac.at/iap/aip/anp/spc-export
Author: Richard van Nieuwenhoven, Markus Valtiner
Author-email: richard.nieuwenhoven@tuwien.ac.at, markus.valtiner@tuwien.ac.at
Project-URL: Homepage, https://gitlab.tuwien.ac.at/iap/aip/anp/spc-export
Keywords: spc conversion
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.1
Description-Content-Type: text/markdown
Requires-Dist: matplotlib>=3.5.1
Requires-Dist: NumPy<2.0

# SPC-Export: SPC Data Conversion for Human and Python Readability

**SPC-Export** is a Python-based tool designed to facilitate the conversion of **SPC** files from Raman measurements into a format that is both human- and Python-readable. This streamlined conversion ensures easy access, readability, and visualization of measurement data for further analysis and interpretation.

SPC-Export was developed to streamline the analysis and sharing of SPC measurement data, making it accessible for scientists and engineers.

Feel free to contribute improvements or report issues to enhance this tool for the Raman community!

---

### ZIP File Contents:
1. **Metadata File**:  
   - **`metadata.xml`**: A detailed XML file containing all header data.  
   - The data is condensed and presented in an easily readable, descriptive format.  

2. **Measurement Data**:  
   - Each measurement is saved as a **tab-separated CSV file** with a header line.  
   - CSV filenames are derived from the measurement descriptions.  

---

## Benefits  

- **Human-Readable**:  
  The condensed XML metadata and descriptive filenames ensure clarity for manual review.  

- **Machine-Friendly**:  
  The CSV format enables seamless integration with Python and other data processing workflows.  

- **Organized Output**:  
  The ZIP file structure ensures all related data is packaged together for easy sharing and storage.  

---

## Applications  

- Post-experiment analysis of SPC measurements.  
- Integration into automated workflows for advanced data analysis.  

---

## Installation and Usage  

1. **Instalation**:  
   - Python >= 3.1  
   - pip >= 22.0.2
   - pip install spc-export  

2. **Run the Script**:  
   ```bash
   spc-export --help

    usage: xps-export.py [-h] [--create-plots] [--create-csv]
                         input_file output_file
    
    Utility for parsing files to XML.
    
    positional arguments:
      input_file      Path to the XPS file.
    
    options:
      -h, --help      show this help message and exit
      
   spc-export test/SGL_reduced.spc
   
   ```
2. **Output**:  
   The ZIP file containing metadata, CSVs will be saved as {output_file}.zip .

## Developers and Designers

   Richard W. van Nieuwenhoven <nieuwenhoven@iap.tuwien.ac.at>
   Markus Valtiner <markus.valtiner@tuwien.ac.at>



