My Awkward questions

[GuillermoFidalgo]

I want to use uproot to access 10 files from OpenData, specifically Higgs to 4 lepton files. How process all these files at once?

[hep-helper[bot]]

Potentially useful sources

15 February, 2023: GitHub issue scikit-hep/awkward#1882, Check ak.firsts/ak.singletons semantics before the 2.0.0 release

Score: 90 out of 100

The conversation provides a detailed explanation of what ak.firsts does, which can help clarify your confusion. Look for the explanation that ak.firsts turns an array of var * X into an array of option[X] by returning the first item of each non-empty list and None for empty lists. This information should help you understand how ak.firsts works in selecting the first element of every event.

6 February, 2021: GitHub issue scikit-hep/awkward#708, ak.singletons not wrapping some arrays

Score: 90 out of 100

The conversation discusses the use of ak.firsts and how it can be a useful alternative to ak.pad_none(a, 1)[:,0]. It also provides examples of how to use ak.firsts in the context of indexing arrays. Look for examples where ak.firsts is used to extract the first element of each inner list in an array. This information can help you understand how ak.firsts works and how it can be applied to your specific use case.

6 February, 2021: GitHub discussion scikit-hep/awkward#710, Removing the 'ak.singletons' and 'ak.firsts' functions: any complaints?

Score: 85 out of 100

The conversation discusses the use of ak.firsts in Awkward Array and provides examples of how it can be used, including handling cases where None values are desired. You can look for examples of using ak.firsts with boolean arrays to select the first element of each event. The conversation also touches on the implementation of ak.firsts and suggests improvements for future versions of Awkward Array.

2 May, 2023: GitHub issue scikit-hep/awkward#2440, Performance regressions compared to ak1

Score: 80 out of 100

The conversation contains information about memory usage improvements in Awkward Array version 2, specifically related to changes in error handling machinery. While it doesn't directly address the ak.firsts function, it provides insights into memory optimization techniques that could be applied to other functions as well. Look for examples of memory optimization strategies and improvements in memory usage to understand how to potentially optimize the ak.firsts function or similar functions.

9 August, 2021: GitHub discussion scikit-hep/awkward#1053, Assigning virtual arrays as a field to an array materializes the virtual arrays

Score: 80 out of 100

The conversation provides insights into the inner workings of Awkward Array, particularly regarding lazy evaluation, memory leaks, and reference cycles. While it doesn't directly address the ak.firsts function, it offers a workaround using the ak.Array constructor to avoid triggering materialization and cyclic references. Look for examples of using the ak.Array constructor to create arrays with virtual fields and how to avoid memory leaks by constructing arrays in a specific way. These concepts can be helpful in understanding and working with Awkward Array functions like ak.firsts.

8 October, 2020: GitHub discussion scikit-hep/awkward#486, Broadcasting the results of two ak.zips

Score: 80 out of 100

The conversation provides a detailed explanation of how ak.zip works in Awkward Array, which can help you understand the behavior of Awkward Array functions like ak.firsts. Look for examples in the conversation where different arrays are zipped together at different depths, and how the depth_limit parameter can control the zipping behavior. This information can guide you in understanding how ak.firsts operates on arrays with nested structures.

7 April, 2021: GitHub issue scikit-hep/awkward#801, Generalize one-argument ak.where to variable length arrays

Score: 75 out of 100

The conversation discusses the functionality of ak.where and potential improvements for handling irregular arrays. While it doesn't directly address ak.firsts, it provides insights into handling variable length arrays. Look for examples of how ak.where can be used with irregular arrays to understand how to select specific elements from events.

13 July, 2021: GitHub discussion scikit-hep/awkward#996, Store smallest deltaR result as new feature

Score: 50 out of 100

The conversation contains information about reducing nested lists in Awkward arrays, which could be helpful in understanding how ak.firsts works. Look for examples where they discuss reducing nested lists to get a single value per event, as this concept is related to what ak.firsts does. While the conversation doesn't directly address your question, it provides a foundation for understanding similar operations in Awkward arrays.

17 February, 2021: GitHub discussion scikit-hep/awkward#745, How to flatten/reduce/search/collect fields into a flat list

Score: 50 out of 100

The conversation provides information on reducing arrays in Awkward, which can be helpful in understanding how ak.firsts works. Look for examples of reducing arrays using functions like ak.mean and ak.max to see how to extract specific elements from arrays. While the conversation doesn't directly address ak.firsts, it provides a foundation for understanding array manipulation in Awkward, which can be applied to your question.

11 January, 2021: GitHub issue scikit-hep/awkward#603, from_buffers/from_arrayset lazyness insonsistent when concatenating

Score: 50 out of 100

The conversation provides insights into how Awkward Array handles lazy loading and slicing operations, which can be helpful in understanding how ak.firsts might work. Look for examples of slicing operations and how they interact with virtual arrays to get a better understanding of how ak.firsts might select the first element of every event.

9 August, 2021: GitHub issue scikit-hep/awkward#1052, Assigning virtual arrays as a field to an array materializes the virtual arrays

Score: 25 out of 100

The conversation does not directly address the question about ak.firsts, but it provides insights into the lazy evaluation of arrays in Awkward Array. To understand how ak.firsts works, you can look for examples of lazy evaluation and materialization of virtual arrays in Awkward Array. This can help you understand the underlying mechanisms of array operations, which may indirectly inform your understanding of ak.firsts.

[hep-helper[bot]]

Potentially useful sources

9 July, 2022: GitHub discussion scikit-hep/uproot5#648, Memory management issues

Score: 80 out of 100

The conversation discusses using uproot to handle large inputs that don't fit into memory, which can be helpful when accessing multiple files. The mention of uproot.lazy and setting object_cache=None in uproot.open() are relevant for efficiently processing multiple files. Look for examples of lazy loading and memory management techniques in the conversation to help process all the files at once.

23 January, 2024: GitHub discussion scikit-hep/uproot5#1098, Every uproot.dask call increases memory footprint by 30 MB (it's in dask.base.function_cache)

Score: 80 out of 100

The conversation discusses memory leaks when using uproot.dask to access multiple files. While it doesn't directly address your question about accessing 10 files from OpenData, it provides insights on memory management and clearing the Dask function cache. You can refer to the examples of clearing the function cache and streamlining the TTree metadata data structure to improve memory usage. This information can be helpful when processing multiple files simultaneously to avoid memory issues.

20 June, 2022: GitHub issue scikit-hep/uproot5#197, Requesting uproot4.num_entries function to quickly get the number of entries without fully reading the file

Score: 80 out of 100

The conversation provides insights into how Uproot 4 handles file opening and metadata extraction efficiently, which can be useful for processing multiple files. Look for examples of how Uproot 4 skips reading unnecessary data until required and how it can be used to gather metadata without fully opening all files. You can use this information to efficiently process your 10 Higgs to 4 lepton files using Uproot 4.

3 November, 2020: GitHub issue scikit-hep/uproot5#173, multiprocessing and uproot4

Score: 80 out of 100

The conversation provides information on using parallel processing with uproot4, which can be helpful for processing multiple files simultaneously. Look for examples of using Python's multiprocessing module with partial functions to process files in parallel. Additionally, pay attention to the mention of customizing the decompression and interpretation executors for better performance. This information can guide you on how to efficiently access and process the 10 Higgs to 4 lepton files from OpenData.

9 March, 2020: GitHub discussion scikit-hep/awkward#153, How to deal with complex combinatorics?

Score: 80 out of 100

The conversation contains information about using uproot to access files and perform analysis, which is directly related to your question. Look for examples and tutorials provided in the conversation, such as the links to tutorials and videos on columnar analysis, Awkward Arrays, and PyHEP. These resources can guide you on how to process multiple files at once using uproot.

30 January, 2024: GitHub issue scikit-hep/uproot5#38, Handle ROOT's memberwise splitting

Score: 80 out of 100

The conversation provides information on how to work with multiple ROOT files using uproot, which can be helpful for accessing the 10 Higgs to 4 lepton files from OpenData. Look for examples of how to open and process multiple files in the conversation, as well as details on TEfficiency objects and byte content that can be useful for understanding how to interact with the files.

24 January, 2024: GitHub issue scikit-hep/uproot5#1093, Every uproot.dask call increases memory footprint by 30 MB (it's in dask.base.function_cache)

Score: 70 out of 100

The conversation addresses memory usage and potential memory leaks when using uproot.dask to access files. While it doesn't directly answer your question about accessing multiple files from OpenData, it provides insights into managing memory usage when dealing with multiple files. Look for examples of clearing the Dask function cache and streamlining the TTree metadata data structure to optimize memory usage when processing multiple files.

13 May, 2022: GitHub discussion scikit-hep/uproot5#597, Best way to process many, long jagged arrays with uproot?

Score: 60 out of 100

The conversation provides insights on how to efficiently process multiple ROOT files using uproot, which can be helpful for your task of accessing 10 files from OpenData. Look for examples of chunking files, concatenating them using uproot.concatenate, and optimizing TBasket sizes and compression algorithms. While the conversation doesn't directly address your question, it offers valuable tips that can be applied to your scenario to improve performance and streamline the process of accessing multiple files.

17 February, 2021: GitHub discussion scikit-hep/uproot5#274, Best Practices/How-tos for Handling Large Amounts of Data?

Score: 60 out of 100

The conversation discusses using uproot to work with ROOT files, which is relevant to your question about accessing Higgs to 4 lepton files. Look for examples of using uproot.lazy to treat a collection of ROOT files as a single lazy array, which could help you process multiple files at once. Additionally, consider exploring the Parquet file format for more efficient data handling.

17 February, 2021: GitHub issue scikit-hep/uproot5#275, Deserialization error in AsStridedObjects but not AsObjects for an example with split level 0.

Score: 50 out of 100

The conversation discusses how to interpret ROOT files with different split levels, which could be helpful in understanding how to process multiple files at once. Look for examples of how to access and interpret data from different ROOT files in the conversation, as well as information on interpreting objects using AsObjects and AsStridedObjects. This knowledge can be applied to processing multiple Higgs to 4 lepton files from OpenData.

2 December, 2020: GitHub issue scikit-hep/uproot5#125, TTree indices awareness in uproot for faster data access from a file?

Score: 40 out of 100

The conversation discusses how to access specific parts of ROOT files using uproot, which may be helpful for processing multiple files at once. Look for examples of using uproot4.open with the minimal_ttree_metadata=False argument to access specific TTree indices in the files. While it doesn't directly address processing multiple files at once, the information on accessing specific data within files can be useful for your task.