Fixing YREC Installation Error: < 600 Rows In .track Files

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Hey guys! Running into an issue when installing YREC with those pesky .track files that have fewer than 600 rows? You're not alone! This guide will break down the error and provide some solutions to get your installation running smoothly. Let's dive in!

Understanding the Issue

So, you're trying to install a custom YREC grid and you've got these .track files. Everything seems fine and dandy until you hit a snag with files that contain less than 600 rows of data. The error message pops up, looking something like this:

    eeps = grids.to_eep(eep_params, eep_functions, metric_function)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/l.morales/anaconda3/envs/stars/lib/python3.12/site-packages/kiauhoku/stargrid.py", line 172, in to_eep
    eep_tracks = parallel_progbar(partial_eep, idx, 
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/l.morales/anaconda3/envs/stars/lib/python3.12/site-packages/kiauhoku/utils/progress_bar.py", line 178, in parallel_progbar
    return [x for i, x in sorted(results, key=lambda p: p[0])]
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/l.morales/anaconda3/envs/stars/lib/python3.12/site-packages/kiauhoku/utils/progress_bar.py", line 148, in _parallel_progbar_launch
    raise x
ValueError: Can only compare identically-labeled Series objects

What's going on here? The core of the problem lies in how the kiauhoku library (specifically, the stargrid.py and progress_bar.py files) handles data processing. The error message ValueError: Can only compare identically-labeled Series objects indicates a mismatch or incompatibility when comparing data structures, likely within the to_eep function or its sub-processes.

Specifically, the to_eep function in stargrid.py is designed to convert tracks into Equivalent Evolutionary Points (EEPs). When your .track files have fewer than 600 rows, it seems like the data processing within parallel_progbar (in progress_bar.py) hits a snag. This usually happens because the data structures (likely Pandas Series objects) being compared don't have the same labels or indices, leading to a comparison error. This can occur if the assumption is that all .track files would have a certain structure, and files with fewer rows deviate from this baseline.

This issue is often related to how the library's internal functions handle edge cases, such as datasets with limited data points. The parallel_progbar function, used for parallel processing with a progress bar, further complicates the debugging because errors within parallel processes can be a bit tricky to trace.

Potential Causes and Solutions

Alright, let's get to the nitty-gritty. Here are a few potential reasons why this error is popping up, along with some fixes you can try:

1. Data Structure Mismatch

Problem: The library expects a certain number of data points or a specific structure in the .track files. When a file has fewer than 600 rows, it might not align with these expectations, leading to inconsistencies in the data structures being compared.

Solution:

  • Inspect the Data: First, take a close look at your .track files with less than 600 rows. Are there any missing columns or unusual data formatting? Open the files in a text editor or use Pandas to read them into a DataFrame and inspect their structure. This will help you identify any discrepancies.
  • Padding: If the issue is indeed the number of rows, you might consider padding the smaller .track files with some form of placeholder data. This is a bit of a hack, but it can work. For example, you could duplicate the last row until you reach the 600-row threshold, or add rows with NaN values if the library can handle them.
  • Adjusting Data Loading: Review the from_yrec function where the .track files are loaded. The skiprows parameter is a good start, but there might be other assumptions made about the data shape or types. Ensure that the data loading process is flexible enough to handle files with varying row counts. It's especially important to check how the data is converted into Pandas Series or DataFrames, as this is where the "identically-labeled" requirement comes into play.

2. Error in to_eep Function

Problem: The to_eep function itself might have a bug or an assumption that doesn't hold true for smaller datasets. This is where the actual EEP conversion happens, so any issue here can be critical.

Solution:

  • Debugging: Dive into the to_eep function in stargrid.py. Use print statements or a proper debugger to trace the data flow, especially around the parallel_progbar call. Check the shapes and labels of the Pandas Series objects being compared. Identify at which point the error occurs.
  • Conditional Logic: Add conditional logic to handle .track files with fewer than 600 rows differently. For instance, you could bypass the problematic code section or use an alternative method for EEP conversion. This might involve adding an if statement that checks the number of rows and branches to a different execution path.

3. Parallel Processing Issues

Problem: The parallel_progbar function might not be handling smaller datasets correctly. Parallel processing can sometimes introduce race conditions or unexpected behavior with edge cases.

Solution:

  • Sequential Processing: As a temporary workaround, try processing the smaller .track files sequentially instead of in parallel. This can help isolate whether the issue is specifically related to parallel processing. Modify the code to skip parallel_progbar and use a simple loop for these files.
  • Progress Bar Handling: The parallel_progbar function includes a progress bar, which can sometimes interact poorly with parallel processing. Ensure that the progress bar doesn’t have any race conditions or locking issues that might affect the data processing. You may need to adjust how the progress bar updates in parallel contexts.

4. Version Incompatibility

Problem: There might be an incompatibility between the kiauhoku library version and your Python environment (e.g., Pandas version). Library updates sometimes introduce changes that affect how data is processed.

Solution:

  • Check Dependencies: Make sure your dependencies (Pandas, NumPy, etc.) are compatible with the kiauhoku version you're using. You can specify version constraints in your requirements.txt file or when using pip.
  • Rollback: If the issue started after a library update, consider rolling back to a previous version that worked correctly. This can help you determine if a recent change is the cause.

Example: Adding Conditional Logic

Let's say you decide to add conditional logic to handle files with fewer than 600 rows. Here’s how you might modify the to_eep function (this is a conceptual example and may need adjustments based on your specific code):

def to_eep(eep_params, eep_functions, metric_function, track_data):
    if len(track_data) < 600:
        # Handle the case for fewer than 600 rows
        eep_tracks = handle_small_track(track_data, eep_params, eep_functions, metric_function)
    else:
        # Original parallel processing code
        eep_tracks = parallel_progbar(partial_eep, idx, track_data, eep_params, eep_functions, metric_function)
    return eep_tracks

def handle_small_track(track_data, eep_params, eep_functions, metric_function):
    # Implement alternative EEP conversion for small datasets
    # This might involve simpler processing or interpolation
    pass

This code adds a check for the number of rows in the track_data. If it’s less than 600, it calls a separate function handle_small_track to process the data differently. You'd need to implement the handle_small_track function to suit your specific needs.

Modified YREC Script Considerations

Since you mentioned you're using a modified version of yrec.py, it’s crucial to revisit your changes. Here are a few things to check:

  • 'eep_params' Dictionary: Verify that the column names in your 'eep_params' dictionary perfectly match the column names in your .track files, especially after any modifications. Typos or inconsistencies here can cause comparison errors.
  • 'parse_filename' Function: Ensure that your naming convention parsing function, 'parse_filename', correctly extracts all necessary information from the filenames, even for files with fewer data points.
  • from_yrec Function and skiprows: The skiprows=9 adjustment is a good start, but double-check that this value is correct for all your .track files. It's possible that some files have a different header length.

Final Thoughts

Debugging these kinds of issues can be a bit of a puzzle, but by systematically checking potential causes and applying targeted solutions, you’ll get there! Remember to thoroughly inspect your data, review your code modifications, and leverage debugging tools to pinpoint the exact source of the error. And hey, don't hesitate to reach out to the kiauhoku community or maintainers if you're still stuck. Happy coding!